Abstract

This paper aims to address how second chances within duty and redemption tie together in the context of medieval knighthood and honor in the poem Sir Gawain and the Green Knight. The poem Sir Gawain and the Green Knight is a poem about second chances and redemption, telling the moral that while mistakes are inevitable, second chances allow individuals to achieve redemption in their duty. Through critical analysis of Sir Gawain and the Green Knight and the character Gawain the Glorious, Gawain’s character is shown to be one who constantly makes mistakes throughout his journeys. Gawain is offered second chances through the form of Lord Bertelak’s game, where his honesty is tested as a knight. Even when Gawain inevitably fails, he is offered partial redemption in the form of a nick he receives on his neck from the Green Knight. This portrayal is significant of a character seen as a model knight. The message in Sir Gawain and the Green Knight uses a previously glorified Gawain to send the message that, despite the expectation of perfection within duty, continuous redemption and improvement through second chances from past mistakes is honest to one’s duty.

In a profession as prized as medieval knighthood, the concept of life, duty, mistakes, and how they interconnect has been a long-debated concept. The relationship between one’s life and one’s duty has been a long-standing question that has been challenged since the beginning of literature. A significant example of this is the famous poem Sir Gawain and the Green Knight, written during the late 14th century, where the relationship between duty and life is explored through the character Gawain, a knight of King Arthur.

Throughout the entirety of Sir Gawain and the Green Knight, Gawain’s role as a regarded knight is continuously tested throughout his adventures. As a knight of King Arthur, he is prized within Camelot. Further, he is a relative of King Arthur himself and is portrayed as a model knight (Jobe). However, within the poem and Gawain’s journey, his mistakes in his duty are ultimately what lead him to survive and redeem himself to live up to his prized identity. Throughout the poem, multiple factors like Sir Gawain’s identity as a knight, the antagonist Green Knight, and Lord Bertalak’s game rest on the underlying themes of second chances and mistakes. Sir Gawain and the Green Knight is a poem about redemption within duty. While mistakes within duty are inevitably made, second chances still frequently present themselves to give opportunities to redeem oneself in their duty while also holding consideration of their life.

The first instance of a mistake leading to a second chance is in the opening scene in Camelot during the feasts of Yule. When the Green Knight approaches the court with a challenge, not a single knight rises to meet him, despite chivalry and bravery being included in a knight’s honor code. This includes Gawain. It is only when Arthur is forced to arise and almost meets the Green Knight’s challenge that Gawain stands up and takes his challenge:

“Gawain bv Guenevere

Toward the king doth now incline:

‘I beseech, before all here,

That this melee may be mine.”

…

“I am the weakest, well I know, and of wit feeblest;

And the loss of my life would be least of any;

That I have you for uncle is my only praise;

My body, but for your blood, is barren of \mrth;

And for that this folly befits not a king,

And ’tis I that have asked it, it ought to be mine,

And if my claim be not comely let all this court judge,

in sight.” (ll. 338-361, Borroff)

In this scene, Gawain stands up and declares, “I beseech before all here that this melee may be mine…if my claim be not comely let all this court judge in sight.” Not only does Gawain claim responsibility as the player in the Green Knight’s game in front of the court, but he also asks the entire court to hold him responsible for his word, which shows he’s committed to seeing the challenge through. Further, in his words against himself, “I am the weakest…and wit feeblest…the loss of my life would be least of any…” In this, he acknowledges his imperfections as a knight and his willingness to sacrifice himself for the court and King Arthur, something that nobody else had stood up for. Despite Gawain’s delayed acceptance of the Green Knight’s challenge, a lapse in his duty to be brave and chivalrous, he swiftly makes it up by taking up and committing to the responsibility of the Green Knight’s challenge from King Arthur’s hands. This shows redemption in his duty and the rest of the court of being an honorable knight despite his initial mistake because he stood up despite nobody else doing so.

Gawain’s next mistake is shortly after his redemption when he takes the responsibility of the Green Knight’s challenge. In his game with the Green Knight, where they exchange a blow for a blow with the Green Knight’s axe, Gawain deliberately makes his cut a fatal one. He was prompted by King Arthur to be intelligent about his blow, as well as tempted by the Green Knight with the outcome of a fatal blow. The Green Knight says: “and if I spend no speech, you shall speed the better: You can feast with your friends, nor further trace” (ll. 409-410). The Green Knight’s words are deliberately tempting — he brings up Gawain’s friends as well as the possibility of him feasting, if he didn’t have to receive a cut in return. Gawain falls for the bait, and by attempting to make it so the Green Knight would not be able to give him a blow back in return, he does not follow the traits of a medieval knight. It does not display bravery or justice to try to slip out of a game Gawain volunteered for, more especially after his poignant declarations to the entire court of his willingness to lose his life. However, the Green Knight survives, and Gawain is also expected to receive the same blow in return as per the rules of the Green Knight’s game. Thus, despite Gawain’s attempts to go through a loophole in the Green Knight’s game being a mistake, it prompts the rest of the journeys, and, at the time for Gawain, unknown second chances for him to redeem himself as a knight as well as his life. This is also explored in Georges Gusdorf’s “The Game of Chance: Moral Ambiguity in Sir Gawain and the Green Knight.” The loophole reflects a larger ethical ambiguity, as Gawain effectively trades his honor as a knight for a better chance to keep his life. Both preservation of life and duty are important values in Sir Gawain and the Green Knight, and the loophole that Gawain utilizes shows the complexity that accompanies abiding by both duty and life (Gusdorf). The poem later reflects:

Now take care, Sir Gawain,

That your courage wax not cold

When you must turn again

To your enterprise foretold. (ll. 487-490, Borroff)

In this scene, the speaker directly addresses Gawain with a declaration that he needs to be courageous in the future. This reads similar to a warning that Gawain has journeys ahead of him that require his courage as a knight, the very thing the game was testing and relying on. It serves as an acknowledgment that Gawain’s future journey is not so simple, because if the journey was just him receiving the cut, he would not need to ensure that his “courage wax not cold.” Further in the story after the year has passed and Gawain’s expected meeting with the Green Knight approaches, he reflects on his future journey.

“There was much secret sorrow suffered that day

That one so good as Gawain must go in such wise

To bear a bitter blow, and his bright sword

lay by.

He said, “Why should I tarry?”

And smiled with tranquil eye;

“In destinies sad or merry,

True men can but try.” (ll. 558-565, Borroff)

This scene gives the reader some insight into Gawain’s emotions and thoughts regarding his journey and his perceived imminent death. He rhetorically asks himself why he should delay his presumed imminent death, then follows it up by saying “True men can but try” when faced with destiny. While it isn’t clear exactly what Gawain is referring to, based on his future destiny to die, and his pondering over why he should delay the inevitable, it can be assumed Gawain is referring to living past his destiny of death. This shows that despite his acceptance of his imminent death, there is still a part of him that wishes to try and live and redeem himself to be a “true man” who would try and live. This offers the reader some insight on the complexities of Gawain’s current emotions, showing that he feels “tranquil.” Despite his duty as a knight and his expected sacrifice, there is still a part of him that wishes to live and have a second chance at life — something that Gawain says a “true man” would do. This implies that Gawain feels that, to be true to himself, he must also try to live. This displays Gawain’s uncertainty in his future journey despite it being something he is bound by duty to since he took up the Green Knight’s challenge, along with consideration of his own life and his hesitancy about death. However, it also shows a development in Gawain’s attitude toward life and duty, as he also considers the preservation of his life despite an imminent death. This is also explored in Marjorie Nicolson’s “Second Chances and Self-Knowledge in Sir Gawain and the Green Knight.” Gawain’s consideration of his imminent death as a result of a mistake shows a sense of maturity, as he still opts to try as a “true man” would, displaying adherence to staying true to his duty despite his situation. Despite his past mistakes, he resolves to move forward with consideration of his duty (Nicolson). Later, when on his journey, he faces a near-death predicament before his duty is fulfilled. The poem reads:

Thus in peril and pain and predicaments dire

He rides across country till Christmas Eve, our knight.

And at that holy tide

He prays with all his might

That Mary may be his guide

Till a dwelling comes in sight. (ll. 733-739, Borroff)

This scene is another example of a second chance at living appearing for Gawain. After trekking to the point where he was on death’s door, he prayed with vigor for a chance to get guided to his destination. His prayers were answered, and soon after, he discovered Lord Bertilak’s court. It’s important that Gawain was on death’s door and was specifically given a second chance because it tells us that not only does he want to live to fulfill his duty, but he was also offered a second chance to do so through his well-timed discovery of Lord Bertilak’s court. This is once again another second chance for Gawain, but also specifically in the context of his necessity to survive to fulfill his duty, providing Gawain an opportunity to fulfill his duty despite his past mistakes when hesitating to take up the Green Knight’s challenge. However, it’s also important to consider whether or not Gawain’s second chance in this case was divine intervention from his prayer to Mary, or a second chance he redeemed from his own actions. But despite the way his life was saved being uncertain, Gawain was still presented with a second chance — whether it was one he seized from his own redemption or one he prayed for, representing a second chance nonetheless. These instances of second chances and Gawain’s uncertainty about his duty and life show the complexities the poem portrays about second chances in duty. The structure of the poem also reflects these repeated trials where Gawain faces second chances, as also analysed in Derek Pearsall’s “The Structure of Sir Gawain and the Green Knight.” The four fit structure shows a section where Gawain is tested once again to face a choice he must make, until finally, in the fourth fit, he’s set to face all of his previous choices and ultimately given a second chance to live, reflecting the structural support of each second chance. (Pearsall)

The next second chance that Gawain takes is when he arrives at the Lord’s court, in the third fit. When initially meeting the Lord, the Lord proposes a game of exchange to Gawain. The poem reads:

“And Gawain,” said the good host, “agree now to this:

Whatever I win in the woods I will give you at eve,

And all you have earned, you must offer to me;

Swear now, sweet friend, to swap as I say,

Whether hands in the end be empty or better.

“By God,” said Sir Gawain. “I grant it fortwith!

If you find the game good, I shall gladly take part.” (ll. 1105 – 1110, Borroff)

This shows the Lord’s offer of a second chance, despite Gawain’s initial ignorance of the true nature of the Lord’s game. However, the Lord’s game adds complexity because it depends on one Gawain’s duty as a knight, which a lack of brought him to his death — he needs to be honest to the Lord. This is a second chance to redeem his duty to better himself from his initial mistake that led Gawain to what he perceived as his imminent death. Gawain, however, successfully engages in the exchange of winnings, taking this second chance for his life and utilizing it. The only exception in this game was the last day, when he did not give the Lord the girdle out of the desire to save his life. The girdle’s role in the games is also significant. In Jane H.M Taylor’s “The Girdle and Its Meaning in Sir Gawain and the Green Knight”, the girdle is shown to be a badge of humility, stemming from a desire to live, but eventually evolves into a partial redemption after Gawain’s mistakes and through his second chances (Taylor). Gawain’s intentions with the girdle and its representation, and Gawain’s honesty regarding the Lord’s game is an interesting juxtaposition between Gawain’s honesty and his mistakes — while he was honest and redeemed himself in his duty by being honest, the mistake he made when he was dishonest stemmed from Gawain’s desire to live. On the third day of the exchange of winnings, when Gawain is considering lying to save his life, the poem reads:

When he gains the Green Chapel to get his reward:

Could he escape unscathed, the scheme were noble!

Then he bore with her words and withstood them no more,

And she repeated her petition and pleaded anew,

And he granted it, and gladly she gave him the belt,

And besought him for her sake to conceal it well. (ll. 1857 – 1862, Borroff)

In this next passage, we can see Gawain deliberately trying to be dishonest and “conceal” what would keep him alive. While the action of Gawain preserving his life is arguably ingrained in human nature, his dishonesty still is not representative of the values that a knight upholds — a mistake in the context of his duty. This mistake is later revisited when Gawain faces the consequences of the Lord’s game. When Gawain came to the Green Chapel to face the Green Knight, who was revealed to be the Lord, he was told he would receive one hit with the axe due to lying on the third day of the Lord’s game. While receiving the stroke, however, Gawain flinches and is criticized by the Green Knight. The poem reads:

“Bestow but one stroke, and I shall stand still,”…

And his [Gawain’s] shoulders shrank a little from the sharp iron.

Abruptly the brawny man breaks off the stroke,

And then reproved with proud words that prince among knights.

“You are not Gawain the glorious,” the green man said,

“That never fell back on field in the face of the foe,

And now you flee for fear, and have felt no harm:

Such news of that knight I never heard yet!

I moved not a muscle when you made to strike” (ll. 2267 – 2274, Borroff)

This passage shows the Green Knight is directly criticizing Gawain’s bravery and courage, which is part of his duty. He denies Gawain being himself, stating that Gawain is “not Gawain the glorious” who had “never fell back on the field in the face of foe.” This is significant, as the Green Knight denying Gawain being himself and “glorious” is him denying Gawain being a good knight who never feared “in the face of foe.” The Green Knight also criticizes Gawain for being a coward, saying “now you [Gawain] flee for fear.” Here, the Green Knight is denying Gawain’s knightly qualities, essentially saying by flinching, he hasn’t been a good knight. This is especially significant considering the honor accompanying medieval knighthood, as explored by Derek Brewer’s “The Ethics of Honor in Medieval Knighthood.” The honor and duty that accompany medieval knighthood were broken by Gawain’s fear of death, as Gawain’s frailty in the face of death highlights his imperfections as a knight. (Brewer) Further, Gawain also does make a mistake prior to his action of flinching because he lies about being still but flinches anyway, saying “I shall stand still” and yet “his shoulders shrank a little from the sharp iron,” representing him making a claim of being brave and not holding himself to it — a mistake in his duty to be brave as a knight. The Green Knight feigns a second hit, in which Gawain doesn’t flinch, and on his third hit, he does not behead Gawain — only nicks him. The Green Knight explains his intentions of the hit, saying:

“I owed you a hit and

you have it; be happy therewith!

The rest of my rights here I freely resign.

Had I been a bit busier, a buffet, perhaps,

I could have dealt more directly, and done you some harm…

True men pay what they owe:

No danger then in sight.

You failed at the third throw,

So take my tap, sir knight.” (ll. 2341-2357, Borroff)

In this passage, after Gawain’s third hit is revealed to be because he lies, as the Green Knight says, “you failed at the third throw.” However, the Green Knight didn’t behead him, showing that one simple nick was enough for Gawain “pay[ing] what [he] owe[d]” and also referred to him as “true man,” implying Gawain’s honesty despite the nick being due to him lying. This implies that if he’d fully lied and been dishonest, he would have been hit thrice — one for a lie on each day — and likely killed. Gawain, in being honest in the game of winnings, had unknowingly redeemed himself from his mistake at the feast last year when attempting to cheat the game by being honest and coming to fulfill the game; he was properly fulfilling what he should be doing as a knight, other than his mistake of dishonesty, which he was punished for. Further, despite his punishment, Gawain was still given a second chance because he wasn’t killed — he still had his life to make up for the mistakes he made of lying. The Green Knight also considers the context of Gawain’s lie, saying:

“So is Gawain, in good faith, to other gay knights.

Yet you lacked, sir, a little in loyalty there,

But the cause was not cunning, nor courtship either,

But that you loved your own life; the less, then, to blame.” (ll. 2365-2368, Borroff)

Here, the Green Knight also directly addresses his dishonesty and says that, despite his dishonesty not being true to his duty as a knight, he can’t be blamed for just wanting to live. The incompatibility of these concepts is also explored in Jesse Roberts’s “Chivalric Duty and Human Fallibility”. Both the honors of knighthood and Gawain’s methodology of preserving his life and his own human fallibility force Gawain to address the tension between these two values. (Roberts) This represents a more nuanced view of Gawain’s lie, building on the portrayal of life and duty in the poem. Despite his duty, Gawain had prioritized his life over upholding the values of his duty. Even then, he is still constantly faced by the weight of his mistakes, but at the same time, given constant second chances to improve and redeem himself. The nick he got for his dishonesty is a final representation of that second chance, and how life ties into duty — he was given a final second chance to live and uphold his duty after learning from the several mistakes he made. Gawain addresses this, saying:

“Your cut taught me cowardice, care for my life,

And coveting came after, contrary both

To largesse and loyalty belonging to knights.” (ll. 2379 – 2381, Borroff)

Gawain directly says he learned more about the duty that belongs to knights by saying the Green Knight “taught [him] cowardice…and loyalty belonging to knights” telling us that his experience prior to the games was lacking but he learned from the entire ordeal with the Green Knight. This shows that his redemption did affect the way he viewed his duty as a knight, and now he has a better view of how to “Care for [his] life” along with harnessing the loyalty belonging to knights. This change in Gawain is also shown in the way the poem describes him. It reads:

Wild ways in the world our worthy knight rides

On Gringolet, that by grace had been granted his life.

He harbored often in houses, and often abroad,

And with many valiant adventures verily he met…

The nick on his neck he naked displayed

That he got in his disgrace at the Green Knight’s hands…

“For where a fault is made fast, it is fixed evermore…” (ll. 2479-2512, Borroff)

Gawain is directly described to be “a worthy knight,” which shows that he is now truly a knight following his duty, and directly addresses his mistake and how it is now “fixed evermore.” His opportunity for this was only possible because of the second chance to live from his duty he got from the exchange game, showing that the message portrayed in Gawain’s character is that redemption in duty is achieved through second chances despite mistakes and mishaps made throughout your life. He did not portray perfection in his duty, but instead had a balance between redemption in his duty and consequences — something that the nick on his neck represents. He did not redeem himself fully, but his partial redemption allowed him to learn from the past mistakes he had made within his duty.

Throughout Sir Gawain and the Green Knight, there is a repetitive and continuous pattern of making mistakes within one’s duty and redeeming oneself. Zooming out, the portrayal of the character Gawain — who is highly praised for being a compassionate and chivalrous knight — openly making mistakes, is unorthodox. These ideas of Gawain as a perfect knight are explored in Kennis Jobe’s “A Model Knight: Sir Gawain, Chivalric Contradictions, and Grief in Medieval Literature.” This different portrayal of him is significant, considering Gawain’s previous portrayal as a model knight in previous works featuring Gawain, such as L’arte Périlleux and Claris et Laris. (Jobe) However, the appeal in a story like this shows in its message of redeeming oneself within duty. It has a certain ignorance toward the concept of perfection at duty and embraces the priority of your life and humanity in the actions of constant improvement, despite past portrayal as a model knight. Ultimately, this poem makes a powerful statement on the complexities of the relationship between life and duty, illustrating a message that, despite the weight of expectations and the want of perfection, the honest way to be true to one’s duty is achieved through constant improvement from past mistakes and seizing second chances to be better.

Works Cited

Borroff, Marie, and Laura L. Howes. Sir Gawain and the Green Knight: An Authoritative Translation, Contexts, Criticism. W. W. Norton & Company, 2022.

Pearsall, Derek. “The Structure of Sir Gawain and the Green Knight.” Speculum, vol. 52, no. 4, 1977, pp. 619–640.

Gusdorf, Georges. “The Game of Chance: Moral Ambiguity in Sir Gawain and the Green Knight.” Modern Philology, vol. 83, no. 1, 1985, pp. 67–78.

Brewer, Derek. “The Ethics of Honor in Medieval Knighthood.” Journal of Medieval History, vol. 14, no. 1, 1988, pp. 39–48.

Jobe, Kennis. “A Model Knight: Sir Gawain, Chivalric Contradictions, and Grief in Medieval Literature.” 2023. Louisiana Tech University, Louisiana Tech Digital Commons, https://digitalcommons.latech.edu/cgi/viewcontent.cgi?article=1104&context=theses

Nicolson, Marjorie. “Second Chances and Self-Knowledge in Sir Gawain and the Green Knight.” Studies in Philology, vol. 82, no. 3, 1985, pp. 281–294.

Taylor, Jane H.M. “The Girdle and Its Meaning in Sir Gawain and the Green Knight.” The Review of English Studies, vol. 56, no. 225, 2005, pp. 362–374.

Roberts, Jesse. “Chivalric Duty and Human Fallibility.” Speculum, vol. 70, no. 4, 1995, pp. 797–820.

About the author

Ananya Kota

Ananya Kota is an 11th grader at Los Altos High School. She interns at her local newspaper as a reporter and is a community editor for her local student-run journalism publication. She is a peer tutor at her school and is the co-founder of a local youth lit-mag.

The post Second chances and Redemption in Sir Gawain and the Green Knight appeared first on Exploratio Journal.

Abstract

This study investigates how gender stereotypes are performed and transformed through viral TikTok trends. While prior research has documented the persistence of gender norms in traditional media, less is known about how they circulate in participatory digital platforms driven by algorithmic visibility. Using a qualitative content analysis of 150 viral TikTok videos across 15 gender-related trends (2023–2025), this project examined both behavioral scripts and identity labels—formats that exaggerate gendered behavior or crystallize personality types. Each post was coded for tone, creator gender, and orientation toward stereotypes, with audience engagement analyzed through comment sections.

Findings reveal four recurring patterns: exaggerated performance, ironic reinforcement, gendered authenticity, and audience co-production of meaning. Together, these show that TikTok functions not as a passive mirror of gender norms but as a participatory system that constantly remixes them. The study contributes to ongoing debates about humor, irony, and digital performance, showing how participatory media both challenge and sustain cultural stereotypes.

Introduction

On TikTok, small jokes can turn into shared language overnight. A creator jokes, “She’s a 10 but keeps five-minute voice memos for every minor inconvenience,” and thousands reply with their own versions. Another sound—“I’m just a girl”—sets off a wave of videos performing femininity with a wink, while the comments debate whether it’s satire or self-own. These aren’t just jokes; they’re the raw material for how people talk about gender.

TikTok has become one of the most powerful cultural stages of our time. Trends that begin as small jokes or offhand performances can spread to millions of people within days, repeating until they feel woven into everyday language. These trends don’t just spread quickly; they invent new ways of talking. Many lean on gender, packaging familiar stereotypes in new forms or inventing fresh labels that quickly spill outside the app and into everyday speech. These labels, along with repeated jokes about behavior, act as a script for how we see one another. What starts as humor begins to shape how people describe each other, how they understand themselves, and how gender is talked about in the culture at large.

This project asks: How do gender stereotypes appear in TikTok’s most viral trends, and what patterns emerge in how they are reinforced, challenged, or redefined across genders? Some of these patterns show up as recurring behaviors—repeated jokes about actions tied to gender. Others take shape as labels or characters, shaping how people talk about each other offline. Both matter. The behaviors show how everyday actions are framed through gender expectations, while labels and characters harden into recognizable identities that can stick to people in everyday conversation.

Key terms. In this paper, scripts refer to repeatable formats that exaggerate behavior (such as jokes or skits), while labels describe shorthand identities like “pick-me girl” or “sigma male.” Both circulate through trends; TikTok’s viral formats are built around shared sounds, captions, or hashtags. Together, they show how the platform transforms gender norms into repeatable and remixable content.

The research design gathered a set of TikTok’s most viral, gender-related trends and analyzed them collectively. By examining both the scripts that exaggerate everyday behaviors and the labels that solidify into identities, the project traced the gendered messages these trends convey, who produces them, and how they shift when applied to mixed-gender contexts. From this analysis, four main patterns emerged: exaggerated performance, ironic reinforcement, gendered authenticity, and audience co-production of meaning. Each reveals something distinct about how gender circulates through TikTok’s participatory culture. 

Exaggerated performance refers to the way creators dramatize gendered behaviors until they become caricatured. These performances make stereotypes visible but also contribute to their recirculation through repetition.

Ironic reinforcement describes the ambiguity that occurs when a creator mocks a stereotype while simultaneously repeating it. Because irony is difficult to detect on TikTok’s fast-moving feed, satire often looks very similar to sincerity, which allows stereotypes to spread even when users intend to critique them.

Gendered authenticity highlights how creators perform “realness” in different ways depending on gender. Women often express authenticity through openness, confession, or vulnerability, while men typically frame it through logic, control, or self-discipline. These differences show that even authenticity follows gendered scripts.

Audience co-production of meaning captures how comments, stitches, and duets shape the interpretation of a post. Meaning becomes collaborative rather than fixed, and audience participation keeps stereotypes active long after the original video is posted.

Together, they show that gender on TikTok is never static but constantly being performed, reinterpreted, and reshaped through humor and participation. The goal was not just to collect examples, but to see the larger dynamics at work: what fades as a passing joke, what hardens into a stereotype, and what gets flipped into something subversive. By recognizing those dynamics, this study aims to show how humor-driven digital culture shapes how gender is understood and talked about today.

Conceptualizing Gender Stereotypes in Media

The media has long served as one of the most influential sites for shaping how gender is understood. Classic theories of gender stereotyping emphasize how portrayals of men and women become cultural scripts that audiences internalize and reproduce (Eagly et al., 2019). These scripts are rarely neutral. Men are typically framed as decisive, assertive, independent, or unemotional, while women are cast as passive, nurturing, or even decorative (Hentschel et al., 2019). These scripts gain power from repetition. Over time, these portrayals appear natural and normalized, giving the impression that they reflect reality instead of actively shaping it.  

Research confirms that gender stereotypes remain persistent across media forms. Santoniccolo et al. (2023) show that mainstream representations continue to objectify and sexualize women, while Lauzen (2018) documents women’s ongoing underrepresentation both on screen and behind the camera on television. Studies of professional settings also reveal similar processes: job advertisements often contain language that subtly reinforces gendered expectations (Sczesny et al., 2024). Even scientific communication is not exempt from this; Chen (2024) finds that controversial science dialogue online often carries gendered assumptions. Together, these studies demonstrate that stereotypes remain embedded in communication, but that leaves a gap in how they circulate in participatory environments like TikTok, where ordinary users rather than producers drive representation. This project addresses that gap by tracking how gender stereotypes and scripts are recycled or reshaped in viral trends on participatory platforms like TikTok.

Humor, Memes & the Reinforcement of Stereotypes

Humor is one of the most enduring ways stereotypes are transmitted. Jokes that target gender may appear harmless, but they reinforce bias by presenting stereotypes as socially acceptable (Ford & Ferguson, 2004). Memes work similarly: they depend on instant recognition, which often means they repeat familiar gendered tropes (Meghana & Vijaya, 2020). Recent studies confirm that sexist memes can influence emotional and moral processing, making discriminative attitudes seem less problematic (Paciello et al., 2021). These findings show that humor and memes play a serious role in stabilizing gender norms. 

At the same time, humor has the potential to destabilize stereotypes. Exaggerating gendered behaviors to extremes can expose how unnatural those stereotypes really are.  (Matamoros-Fernandez, 2023) shows how features like duets, stitches, and sound reuse encourage endless remixing of jokes, sometimes reinforcing stereotypes and sometimes tearing them down. Meme analyses by Sultana (2025) and Mihailescu (2024) show that memes can be flipped to question or debate social roles. This tension, between reinforcing and challenging, is significant to TikTok humor, which dominates viral content. This project, therefore, examines not only how jokes reinforce gender stereotypes but also when and how they create cracks.

Digital Labeling in Online Culture

Online culture frequently condenses complex identities into shorthand labels. Nilsson (2024), for instance, documents how the “I’m just a girl” TikTok trend packaged gender performance into a recognizable cultural script, while Tanner (2025) analyzes “sigma male” discourse as a form of toxic communication that reframes masculinity. In both cases, labels crystallize stereotypes while also creating opportunities for new forms of identity work. 

Labels are rarely descriptive alone. They almost always will imply judgment. To be called a “pick-me girl” means being critiqued for seeking male validation, while “sigma male” often mixes parody with aspiration. Huber and Baena (2023) show that women scientists on TikTok sometimes embrace labeling strategies to increase their visibility, though it risks reinforcing old stereotypes about women in STEM. Steinke (2024) expands this, noting that female creators both embrace and resist labels as part of identity work. Yang (2023) conceptualizes this process more broadly, describing memes and labels as “cultural scripts” showing how memes and shorthand terms shape identities online. These examples show how online labeling both simplifies and amplifies identity work. On TikTok, those labels spread even faster because anyone can adopt, parody, or remix them. This project follows that insight by examining how viral TikTok labels work to reinforce, reshape, or challenge stereotypes.  

TikTok’s Amplification of Gendered Trends

What makes TikTok different from earlier forms of media is its speed and its algorithm. Its “For You Page” curates content in ways that don’t just reflect popularity but actively construct it (Varmazyar & Cardama, 2023). Dillon et al. (2023) found TikTok content often reproduces bias, while Yin and Abdullah (2024) found that negative portrayals of women spread quickly through trending content. In other words, stereotypes on TikTok are not just reproduced, but they are accelerated by design. 

At the same time, TikTok’s constant remixing changes how these trends play out. A single sound may generate thousands of versions. some repeating stereotypes, others mocking them. Remixing matters because it’s how meaning gets made on TikTok: each reuse of a sound or caption gives an old idea new life, sometimes as humor, sometimes as critique, and sometimes as both at once. Suarez-Garcia et al. (2024) demonstrate how sexism appears in multimodal video data. Matamoros-Fernandez and Farkas (2023) highlights how humor and amplification interact to make harmful content that becomes both visible and normalized. Yet platforms like TikTok can also enable resistance. Huber and Baena (2023) show how women scientists use TikTok to disrupt expectations and assert their own new forms of visibility. Taken together, this research positions TikTok as both an amplifier and medium for resistance, reflected by their spreading of stereotypes at scale, while also simultaneously offering space for rebuttals. This study builds on that work by examining how gendered scripts and labels circulate in viral trends and whether they are reinforced, parodied, or redefined.  

Data and Methods

TikTok as a Research Site

TikTok has become one of the fastest-growing social media platforms over the past five years, with estimates projecting its global user base to exceed 1.5 billion monthly active users by 2025 (Business of Apps, 2025; Boeker & Urman, 2022). Unlike traditional media, TikTok is participatory, meaning users don’t just consume content; they actively produce and remix it. TikTok’s duet and stitch features, tools that allow users to place their videos alongside or directly after another creator’s post, also make trends highly interactive, enabling users to respond, critique, or extend a trend in ways that shape how gender stereotypes circulate. This makes it a uniquely useful site for studying gender stereotypes. Prior scholarship has shown how stereotypes are embedded in traditional media like television, film, and advertising (Goffman, 1979; Lauzen, 2018), but TikTok provides an opportunity to observe how those scripts spread, change, and gain traction in real time. While TikTok’s algorithm is central to this process, it is not transparent and cannot be studied directly. Instead, this project examines its manifestations: the trends that receive disproportionate visibility and circulate widely on the platform. As a young woman familiar with TikTok culture, I approached this research both as a participant and observer. My interpretations were informed by an understanding of platform humor, but I aimed to stay reflexive about how my own perspective might shape what I noticed. Because TikTok’s algorithm determines visibility in opaque ways, examining trends themselves, rather than trying to access the system behind them, became the most effective way to study how gendered content gains traction on the platform.

Defining and Selecting Trends

On TikTok, a “trend” refers to a repeatable format that thousands of users adapt with their own variations, often built around a shared sound, hashtag, or caption (Ling et al., 2021). In this study, I focus on two types of trends. The first are scripts, which take the form of jokes or skits that exaggerate a behavior. The second are labels, short phrases that mark out an identity or archetype. These trends matter because they reduce gender stereotypes into recognizable forms that can easily be repeated and remixed, ensuring their circulation across diverse audiences. Each reuse or remix of a trend pushes the stereotype further, sometimes reinforcing it and sometimes twisting it into parody or critique (Meghana & Vijaya, 2020).

Sampling Strategy

For this project, I examined viral trends from 2023 to 2025. Because TikTok does not provide a fixed threshold for virality, I developed my own operational definition: a trend counts as “viral” if an individual post reaches at least 100,000 views or 10,000 likes. These thresholds were chosen because they indicate visibility beyond a creator’s immediate follower base and align with prior research that uses engagement ratios as markers of wide circulation (Zhou, 2024).

Trends were identified through two complementary strategies. First, I used an “influencer-first” discovery, beginning with well-known creators across niches. Second, I used trend-first discovery, searching TikTok’s sound pages and hashtag feeds to locate formats gaining traction across multiple creators. To determine whether a trend was spreading beyond a single creator network, I examined whether the same sound or hashtag appeared across diverse accounts, especially when creators were unconnected by follower networks or genre. Using both strategies allowed me to capture trends that spread top down through influencers as well as those that emerged more organically from everyday users.

Dataset and Coding

The dataset included 15 viral trends, each represented by approximately 10 posts, for a total of about 150 posts. These trends reflected both male-coded and female-coded stereotypes. Before selecting videos for analysis, I applied several exclusion criteria: videos in languages other than English, reposts or spam content, advertisements, and posts where either creator’s gender or target gender could not be reasonably inferred. These exclusions helped keep the sample consistent and ensured that all posts could be analyzed using the same categories.

The dataset included 15 viral trends, each represented by approximately 10 posts, totaling around 150 posts. These trends reflected both male-coded and female-coded stereotypes. Each post was logged in a Google Sheet, with one row per video and the following structured fields:

• Trend Name: The viral format or meme category
• URL: The direct link to the post, ensuring retrievability
• Creator Gender: The gender identity that the creator presented in the video or on their profile, recorded as male, female, both (for group accounts), or unclear
• Target Gender: The group toward whom the stereotype, label, or joke was directed, recorded as male, female, both, or unclear
• Tone: The manner in which the stereotype was delivered, categorized as serious, ironic, parody/satire, or ambiguous
• Valence: The overall orientation of the post toward the stereotype, recorded as reinforcing, countering, or mixed
• Notes: A free-text column where I recorded my own observations about each post
  

The “Notes” section included details that did not fit neatly into categories—such as how the creator used captions, body language, or sound; whether the performance exaggerated a stereotype for humor; or if the video seemed to blur critique and reinforcement at the same time. In addition to my own interpretation, I also noted patterns in audience response by scanning top comments and engagement. These reactions provided insight into how viewers were reading the video—whether they laughed along, pushed back, or reinterpreted it. Together, this combination of creator performance and audience reception gave a fuller picture of how gendered trends were being interpreted on TikTok.

Because this project was conducted by a single researcher, no formal inter-rater reliability test was possible. Instead, I used a reflexive coding process to strengthen consistency. After coding the first 30 posts, I revisited my original labels for tone, valence, and target gender, compared them with later entries, and refined the category definitions to reduce ambiguity. I then re-coded a subset of earlier videos using the updated definitions to ensure that my interpretations were stable over time. While this is not a substitute for multi-coder reliability, it provides a structured way to check for consistency in a single-researcher qualitative study.

Table 1. Descriptive Overview

The “hybrid” tone category included videos where creators shifted between sincerity and parody, or where tone could not be cleanly separated because irony and seriousness were blended within the same performance.

Table 2. Overview of the 15 TikTok Trends Included in the Dataset

Sample of Composition

The final dataset included 150 posts drawn from 15 distinct viral TikTok trends, with around ten posts collected for each. Together, these posts captured how gendered humor, labeling, and performance show up in TikTok’s most visible spaces between 2023 and 2025. As shown in Table 1, about half of the videos were created by female-presenting users, around a third by male-presenting users, and the rest by creators whose gender presentation was mixed, collaborative, or not clearly identifiable. This makes sense given TikTok’s overall user base and the dominance of feminine-coded humor, lifestyle, and commentary genres that tend to drive virality on the platform.

When looking at tone, roughly one-third of the videos were serious in how they portrayed gender roles, while about 27 percent used parody or satire. A smaller portion blended humor and sincerity—often flipping between the two in the same clip. This mix of tones captures something distinct about TikTok itself: the way sincerity and irony constantly overlap. The same sound, caption, or format can mean completely different things depending on who performs it and how. That’s part of what makes these trends so powerful—they invite both imitation and reinterpretation.

In terms of valence, nearly half of the videos reinforced familiar gender stereotypes, while others either pushed back against them (around 14 percent) or combined both reinforcement and critique (about 38 percent). The overlap between these categories is important. It suggests that even when stereotypes are being “mocked,” they still circulate widely and can easily be taken at face value. Many creators seemed aware of this tension and leaned into it, using humor or exaggeration to test how far they could go without fully endorsing what they were representing.

Finally, the Notes section helped fill in what the numbers couldn’t show. Here, I recorded details about performance style, creator intent, and audience reactions—especially the comment sections, where people often made the meaning of a post explicit. Sometimes viewers laughed along and reinforced the joke; other times, they called it out or reinterpreted it. These responses often shifted how a post could be read, showing how meaning on TikTok is a shared construction between creator and audience. Together, these observations point to a platform where gender norms are not just displayed but constantly negotiated, blurred, and remixed in real time.

Results

From the 150 TikTok videos analyzed, several key patterns emerged in how gender stereotypes were performed, shared, and interpreted. Before turning to those patterns, it helps to understand what these “trends” look like in practice. On TikTok, a trend is usually a short, repeatable format — often a joke, sound, or role-play — that thousands of users adapt with their own twist. Many of the gendered trends in this study use humor and exaggeration to explore how men and women are expected to behave. For instance, videos under “She’s a 10 but…” or “The Ick” dramatize the ways people judge one another in dating contexts, while trends like “Pick-Me Girl” or “High Value Man” assign labels that turn personality types into familiar online characters. Others, such as “Female vs. Male Gaze” or “Almond Mom,” reflect how gender norms appear in everyday self-presentation, work, or family life. Across these formats, users continually remake the same cultural scripts, sometimes to mock them, sometimes to affirm them, and often doing both at once.

From this dataset, four major themes emerged: exaggerated self-awareness and performance, ironic reinforcement, gendered claims to authenticity, and the audience’s co-production of meaning. Together, these patterns show how TikTok doesn’t just repeat existing stereotypes, but constantly reworks them through humor, remixing, and audience participation.

Exaggerated Self-Awareness and Performance

Across multiple trends, creators didn’t simply show gendered behavior; they performed it to the point of caricature. In trends like “She’s a 10 but…”, “The Ick,” and “Girl Math,” humor depended on exaggeration. Creators amplified familiar traits—overthinking, dramatizing, emotional detachment—until they became absurd. For example, in “Girl Math,” women jokingly justify impulsive spending (“It was 50% off, so I basically made money”), while in “The Ick,” users overact disgust at minor habits (“He ties his shoes weird—ick”). These portrayals are not realistic; they’re intentionally over-the-top, designed to make the audience both laugh and recognize the stereotype behind the joke. 

This kind of exaggerated self-awareness often works as a form of social commentary. It lets creators acknowledge the absurdity of gender expectations while still participating in them. By performing stereotypes knowingly, users expose them whilst also keeping them alive through repetition. In the comments under these videos, audiences frequently responded with agreement rather than critique (“So real,” “That’s literally me”), showing how irony can slide back into affirmation. 

The humor here cuts both ways. It creates a shared understanding between creator and viewer—we all get the joke—but that familiarity is also what keeps these stereotypes circulating. Each reuse of the format reinforces the same gender-coded behaviors, even when the intention is to mock them.

Ironic Reinforcement

If exaggerated performance makes gender norms visible, irony makes their meaning more ambiguous. Many creators in this dataset used humor as a kind of protective layer—saying, in effect, “I’m joking, don’t take this seriously.” But that same irony often allowed stereotypes to spread unchecked. In trends like “Pick-Me Girl,” “High Value Man,” and “Looksmaxxing,” irony blurred the line between critique and endorsement. 

In “Pick-Me Girl” videos, for instance, women acted out exaggerated lines such as, “I’m not like other girls, I watch football,” using mocking tones or overdone gestures to expose how women are sometimes rewarded for rejecting femininity. Yet, in the comments, viewers often missed the irony. Many men responded earnestly (“Finally a girl who gets it”), revealing how satire can reinforce the very logic it aims to critique.

A similar pattern appeared in “High Value Man” and “Looksmaxxing” trends. Some male creators mimicked self-improvement influencers, listing ways to “raise your SMV”—social or sexual market value—as a form of parody. Others delivered nearly identical advice seriously, treating dominance, appearance, and wealth as measures of worth. Because TikTok’s format encourages repetition and remixing, both versions used the same sounds, captions, and imagery. A viewer scrolling quickly might not know which is which.

This ambiguity is central to how irony functions on TikTok. It gives creators plausible deniability—they can always claim, “It’s just a joke.” But when those jokes are repeated and liked thousands of times, they take on their own cultural weight. As researchers such as Ford and Ferguson (2004) argue,

 disparagement humor lets bias hide behind laughter, which can normalize prejudice by making it seem socially acceptable. On TikTok, that process occurs faster and has a wider reach.

Gendered Claims to Authenticity

Another major pattern that emerged was how creators performed “realness” differently across gender lines. On TikTok, authenticity functions almost like a currency — the more “real” or “relatable” someone appears, the more credible their message becomes. But what counts as authentic often depends on who’s speaking and how.

In trends such as “Almond Mom” and “Female vs. Male Gaze,” female creators often grounded their authority in emotional honesty and lived experience. In “Almond Mom,” for instance, women reenacted moments of being policed about food or body image — lines like “You really need all that?” or “I just don’t eat carbs after lunch” — to highlight how controlling, diet-obsessed parenting gets normalized as “care.” The humor was sharp but personal, inviting viewers to laugh while recognizing a familiar discomfort.

In “Female vs. Male Gaze,” creators compared what women find attractive versus what men find attractive — for example, showing a man in a cozy hoodie and messy hair (“female gaze”) next to the same man posing shirtless in a gym mirror (“male gaze”). The trend evolved into commentary on whose preferences define beauty and whose approval people are dressing or performing for. A lot of women used the trend to take back control, showing that feeling good and expressing personality can be more attractive than trying to look flawless.

Male creators, by contrast, tended to frame their authenticity through logic and control. In “Looksmaxxing” or “High Value Man” videos, they spoke in the language of optimization — “Improve your jawline with this daily routine,” “Dress in neutrals to look more put-together” — as though authenticity were something that could be engineered. This style mirrors what sociologist R.W. Connell (2005) describes hegemonic masculinity, where credibility comes from rationality and self-discipline rather than emotion.

These contrasting portrayals show how gender is performed not only through appearance but also through the emotional and social expectations tied to each gender. In “Female vs. Male Gaze,” humor exposes how beauty standards shift depending on who is doing the evaluating, and how women often must balance both sets of expectations at the same time. The joke works because viewers already understand this double bind: men are often taught to value polished or sexualized presentation, while women tend to emphasize comfort, personality, or everyday attractiveness. The humor becomes a form of critique, allowing women to exaggerate the contrast and reclaim a sense of agency by highlighting the superficiality of male standards.

Male creators, in contrast, frequently use seriousness or technical language to signal credibility. Their content rarely invites the same kind of playful self-reflection, which reinforces the idea that masculine identity is tied to control, logic, and improvement. This difference shows that what counts as “authentic” is never neutral. It is shaped by cultural expectations about how men and women are supposed to present themselves, what emotions they are allowed to express, and how they should demonstrate social value.

Audience Co-Production of Meaning

Finally, one of the most striking patterns in the data was that meaning on TikTok didn’t stop once a creator uploaded a video; it kept evolving through the comments, duets, and stitches that followed. The platform’s participatory design means that audiences aren’t just passive consumers; they’re active interpreters who shape how a post is understood. In some cases, they even flipped its meaning entirely.

For example, under videos using the “Trad Wife” hashtag, women presented domestic life through soft, vintage aesthetics like baking bread, wearing long dresses, and captioning posts with phrases like “romanticizing my life” or “making the house a home.” The comments, though, showed how differently people read the same video. Some users wrote, “This is so peaceful, I want this life,” while others fired back with, “Girl, you’d last two days without Wi-Fi.” What one viewer saw as wholesome, another saw as regressive.

A similar tension appeared under “Sigma Male” posts, where creators paired composed, slow-motion clips with motivational quotes or dark instrumentals. Some commenters mocked the tone–“bro thinks he’s in a trailer,” “POV: he ordered a black coffee once,”– while others shared the message, tagging friends and writing “real mindset.” In both cases, the humor and sincerity overlapped so tightly that it became hard to tell which was which.

Even lighter trends like “She’s a 10 but…” or “The Ick” turned gender stereotypes into an interactive game. What started as quick jokes, “He’s a 9 but claps when the plane lands,” evolved into a couple of interviews, where creators asked their partners to rate them or their exes based on trivial habits. These videos often blurred humor and discomfort: laughing through the process of being judged, while reinforcing the idea that someone’s worth can be ranked on a 1–10 scale. The comment sections amplified it further, with users chiming in to defend, mock, or one-up each example. The format made it easy for everyone to join in, but it also normalized the constant evaluation of behavior and appearance through a gendered lens.

Taken together, these patterns show that TikTok operates both as a mirror that reflects gender norms and as a generator that reshapes and intensifies those norms through viral repetition.. Its humor, speed, and remix culture allow stereotypes to mutate rather than simply repeat. Exaggeration turns expectations into shared jokes, irony disguises reinforcement as critique, authenticity becomes a gendered performance, and audiences collectively decide what each post means. The result is a feedback loop in which meaning is never fixed—gender is continuously performed, debated, and redefined in real time. This constant negotiation gives TikTok both its cultural influence and its ambivalence. The platform can challenge gender stereotypes by drawing attention to their absurdity, yet it can also reinforce them through repetition, humor, and algorithmic amplification. Gender norms on TikTok are therefore unstable. They are continually being tested, stretched, and reassembled in ways that influence how young people interpret what it means to be masculine or feminine today.

Discussion and Conclusion

Overview

This project set out to understand how gender stereotypes appear and evolve inside TikTok’s viral culture. While earlier research has shown how traditional media embed gender roles through repetition, this study shows that TikTok works differently. On this platform, gendered scripts don’t simply appear in finished form; they’re constantly being performed, joked about, and reshaped by millions of users in real time. TikTok turns stereotypes into something participatory. This participatory quality matters because it shifts who controls representation. In traditional media such as television or film, stereotypes are created by producers and writers and then delivered to audiences. On TikTok, ordinary users participate in generating, repeating, and revising those stereotypes. This makes gender norms more fluid, but also more widespread. Once a joke becomes a trend, thousands of people join in, which can turn a stereotype into shared language within days. Participation democratizes representation, but it also accelerates how quickly stereotypes spread and take root. Because viewers can immediately respond, reinterpret, or remix a creator’s video, audience participation becomes central to how stereotypes persist and evolve on TikTok.

Irony as an Unstable Form of Critique

Irony is no longer a guarantee of critique. Many of TikTok’s gendered trends rely on humor to signal self-awareness, allowing creators to distance themselves from the stereotypes they perform. But irony, in this context, is unstable. On a platform driven by repetition and speed, tone becomes more difficult to identify; satire and sincerity often look identical. When viewers encounter a video out of context, what was meant as parody can easily be read as endorsement. This suggests that irony, rather than protecting against stereotypes, can sometimes make them more durable by making repetition look like critique.

Authenticity as Gendered Performance

Authenticity is performed through gendered expectations. TikTok’s culture emphasizes “realness,” but what counts as authentic is deeply gendered. Female creators in this dataset often performed authenticity through emotional openness, confession, or vulnerability, while male creators tended to perform authority, control, or rational expertise. Both versions of “realness” might look spontaneous, but they’re still performances. What feels authentic on TikTok is often what the algorithm rewards and what audiences respond to. In that way, authenticity isn’t an escape from performance; it’s another form of it. Instead of showing who people “really are,” the platform creates a new kind of script for seeming genuine—one that is filtered through gender expectations and audience approval.

Audience Participation and the Circulation of Stereotypes

Audience participation sustains stereotypes. Meaning on TikTok doesn’t stop when a creator uploads a video; it keeps evolving through the comments, duets, and stitches that follow. Audiences, instead of just consuming gender norms, help produce them. Comment sections often turned jokes into debates or reinforced the stereotypes the creator was trying to mock. Even disagreement kept the trend in motion by boosting engagement and visibility. This shows that participation itself is what gives stereotypes their staying power. On a platform where attention equals reach, every like, repost, and argument contributes to a cycle that keeps these stereotypes circulating.

The Collapse of Tone

The collapse of tone creates ambiguity and power. In earlier forms of satire, the stance was clear—the audience could tell that the creator didn’t actually believe what they were portraying. On TikTok, that clarity often disappears. The same sound or format might be used both sincerely and ironically, and a viewer scrolling quickly might not know the difference. This tonal collapse gives the platform creative energy but also allows stereotypes to circulate without accountability. At the same time, viewers can project their own beliefs onto a trend, reading sincerity where none was intended. This collapse of tone helps explain why TikTok content spreads so fast and why stereotypes can reappear in new, more ambiguous forms.

Limitations and Future Research

This study focused on a relatively small sample of 15 viral trends and 150 TikTok posts, chosen to represent the most visible examples of gendered content. Because of that, the findings shouldn’t be taken as representative of all of TikTok or of every kind of user community. The analysis also centered on English-language videos from mainstream creator networks, which leaves out regional and subcultural spaces where gender performance might look very different.

Future research could build on this work by studying a larger or more diverse dataset, including creators from other linguistic or cultural contexts. It could also combine qualitative analysis with computational tools to track how trends spread over time, or explore how algorithmic amplification shapes which performances of gender gain visibility. Finally, interviews with creators and viewers could help explain how people understand their own participation—whether they see it as parody, sincerity, or something in between.

Overall Implications

More broadly, this study shows that gender in the age of social media is not just represented but actively produced through everyday participation. On TikTok, gender norms do not sit still; they are constantly negotiated through humor, repetition, remixing, and audience interaction. This makes stereotypes more dynamic and adaptable than in traditional media, but also more resilient. They spread faster, mutate more easily, and can be reinforced even as they are being mocked. At the same time, the platform creates space for resistance, allowing creators to expose contradictions in gender expectations or reframe them in ways that feel more authentic. The major takeaway is that participatory media like TikTok turn gender into an ongoing public performance shaped by millions of small creative acts. Understanding this process is essential for recognizing how cultural beliefs about gender are forming today, and how young people encounter, challenge, and internalize them in real time.

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About the author

Kaira Krippendorff

Kaira is a student researcher passionate about sociology, digital media, and gender studies. Her work focuses on how online platforms influence cultural norms and shape young people’s understanding of identity. She hopes to continue exploring the social impact of emerging technologies through future research.

The post Remixing Stereotypes: How Gendered Scripts Evolve Through TikTok’s Viral Culture appeared first on Exploratio Journal.

Abstract

War is a major issue in our current world, with 56 current conflicts around the world, the most since WWII. This is extremely concerning, as most people are not aware of most wars or coups that happen in different countries. A particularly unstable region in this age is the Middle East and North Africa. This study attempts to predict the onset of wars and coups in the MENA region by using economic and weather data and comparing it to other economic and weather data captured during times of conflict in these particular areas. A pipeline classification was built with weather and economic data from various time periods in places that were in a conflict at the time. Articles on events in these regions were also collected to gauge the situation at any given time. The model would then fit the data as well as the article features and output the likelihood of war in a particular area based on the economic and weather data it received. If we can predict wars and other conflicts before they can even happen, we can better prevent them, or decrease the casualty rate. While this is to some extent a report of work done, it is also a proposal for future work.

1 Introduction

Although wars and coups have occurred consistently throughout history, the frequency and intensity of such violent conflicts have notably increased in recent years. In 2021, there were 27 ongoing conflicts, with the number steadily rising every year (Koop 21; Rustard 24). Today, the world is experiencing the most conflicts and wars it has faced since WW2 (Archie 22). Although there are many terrible events happening around the world, affecting different countries everywhere, the general public only knows about a few of them, and most are unaware of how drastic they are.

Unfortunately, this also means that there isn’t too much news coverage and aid for the issues faced by countries experiencing lesser-known conflicts, as sometimes it gets too dangerous in certain areas for people to help (Burchell 2020). This model can be utilized to identify early indicators of unrest and the onset of conflict in specific regions, enabling proactive intervention to mitigate escalation. Ideally, specialists and humanitarian organizations would have the ability to anticipate areas at risk of instability, allowing for preemptive measures that reduce the potential for widespread harm. While there is a possibility that such a model could be misused by authoritarian parties to better monitor and suppress dissent, this risk can be managed with ethical oversight and responsible implementation. This paper demonstrates a program that could allow for the prediction of future conflicts in the MENA region using climate and economic data.

2 Literature Review

The purpose of this literature review is to determine which factors play an important role in causing conflict, and ways we can look for such factors in advance to prevent such events. Nations have declared war on other nations or groups for a variety of reasons. Some may have been more abstract, such as conflicts between religion or culture, while others were also more concrete reasons, such as the need more land, population, or resources. However, because of World War II, war and conflicts in general have changed irrevocably. Now conflicts are characterized by better surveillance technologies, with new inventions such as drones and more accurate weapons. They also tend to involve many civilians now, rather than just soldiers, and often involve various factions, some which aren’t state-affiliated. In this new post-World War II era, conflicts have started to be coined by US analysts as Fourth-Generation Warfare (Holbrook 20). This era has also seen a slow decline in foreign wars, yet the number of civil wars has started to rise, with the number of such conflicts having tripled since the last decade (von Einsiedel 2017).

A civil war, defined by Britannica, is a conflict that involves the clashing of multiple organized non-state actors, which differs from interstate, or foreign wars, which are wars declared on one nation by another. There are two wars that have the world’s attention currently, the Russo-Ukrainian and Israel-Palestinian Conflicts. Although some parties claim otherwise, both happen to be foreign wars, conflicts waged by one functional government against another. Although both are classified as interstate wars, their occurrence represents a deviation from the declining trend of foreign conflicts, and their prominence in global discourse can be attributed to their relative rarity compared to the numerous civil wars that commonly affect failed states.

Despite the fact that it seems as though there are a plethora of differences between the two types of conflicts, in Lemke and Cunningham’s 2009 paper, they come to the conclusion that there isn’t a point to making distinctions between the two. Despite this, distinguishing between the two types could provide more insight, and is something to look into. For now, though, research should instead prioritize evaluating their overall effectiveness as catalysts for conflict. Although there are a lot of different factors that could influence the decision to declare war, there isn’t a definitive determinant that inevitably leads to war. Since there isn’t one deciding factor as to why all conflicts start, many researchers attempt to search for one factor or a set of factors that have the biggest impact as to whether wars start or not.

According to Stewart (2002), war is strongly influenced by the economic is sues of a nation and its surrounding area at a given time. His research paper, ”Root causes of violent conflict in developing countries”, focuses on 4 big economic hypotheses, and how these factors could possibly contribute to the start of intra-state wars in the modern era: Group Motivation Hypothesis, Private Motivation Hypothesis, Failure of the Social Contract, and Green War Hypothesis. While the paper had very in depth information on how economics could cause such conflicts, it excludes many other factors, and doesn’t go into detail concerning political, religious, cultural, resource, geography, or climate related factors. Although economics undeniably plays a major part in the inner and foreign situation of a state, it shouldn’t be the only factor. Another thing that has changed between 2002 and the present day is the US’ involvement and policies regarding the Middle East region after 9/11 (Esfandiary 2021). Although this article was written post-9/11, it ignores big changes in the political landscape of the Middle East and before the true consequences of the US’ actions had been realized. Despite this, it still is applicable to some degree as the economic consequences mentioned in Stewart’s writings would still hold major influence in a nation’s stability and tendency to declare war.

Coccia (2019) explains many different theories that have been used to explain how and why wars happened. She elaborates on both historical and modern theories on the cause of war and conflict. Modern theories could explain or uncover a big part of why such conflicts happen, and the inclusion of past theories allows insight into how modern theories could have developed. The contents of the paper are mostly theoretical, finding reasons that countries would declare war and other theories relating to the nature of conflict. Although this paper focuses on foreign wars, and doesn’t mention the case of a civil war, it has still provided valuable information on why conflicts could happen, and the logic behind how they could start. Even after someone has determined what cause of war they want to further research, there are a variety of ways for them to find, categorize, and store the data they use.

In order to find reasons for involvement in wars, Makarov (2015) uses statistics. He takes data from times when a certain nation is either at war or in a time of peace. The paper considers political (e.g. diplomacy), economic, and religious factors that could influence the likelihood a country would be in a state of warfare. Once Makarov obtains the data, statistical models and distributions are used to then gather the data needed to build graphs and make projections of the previously mentioned factors and their effects. However, the way statistics are used in Makarov’s case differs from this paper’s approach to utilizing statistics and data. While Makarov mainly focuses his paper on why certain nations could be involved in wars, this paper attempts to facilitate prevention of such conflicts by picking up on trends that have been seen before and predicting the likelihood of conflict in a certain area.

3 Methods

This paper primarily examines the economic and climatic factors that contribute to the onset of war, analyzing their correlation with reported events in news articles. The model does this by utilizing features extracted from articles to assess the severity of conflict in a given region at a given time and integrates economic and climate data to evaluate their potential influence on the situation. Research from the UN shows that climate is important to consider as it could determine whether or not a country could face civil unrest, or even wage war against other nations. If a country has been facing climate that isn’t typical for the region over an extended period of time, it could cause an assortment of problems. In one report from the UN in 2021, they state that extreme weather and climate changes, along with other factors such as disease, have caused many droughts and subsequent food shortages for places such as Latin America, Asia, and Africa (UNFCCC 2021). There have been similar trends before and during the outbreak of war as well, with droughts and food shortages being caused by drastic climate change in a region. This could cause leaders to fight for precious resources or cause parts of the country to revolt against the central government. Economics could also be an important part to consider, as harsher economic times could cause unrest, and increase the chances of conflict, whether it be from disgruntled civilians or a government desperate to alleviate itself from these issues.

The code for the prediction model first gathers both economic and climate data, as well as articles and events corresponding to different times during the war. These data points are what the model later trains and tests on to gauge the accuracy of its results. This model is trained on data from the Darfur conflict and Syrian civil war occurring in Sudan and Syria respectively, although more conflicts in the region could be added for more complex and accurate predictions. In order to encode the articles into a readable state, qdrant’s FastEmbed, an embedding model was used to turn raw data (articles) into vectors from 1 to 1 using cosine similarity of article feature vectors (qdrant Version 0.6.0). The outputs/events, originally represented as strings (e.g. ’war’ or ’coups’), are transformed into k-hot encoded vectors to facilitate data visualization. This transformation employs a multi-label k-hot encoding approach, allowing for the representation of multiple events occurring within a single time period, thereby enhancing the clarity of the data.

Before training and testing the data, it is necessary to interpolate periods with no events or articles, or other data. The analysis period is defined from the earliest to the most recent data point, with all intervening empty time periods requiring interpolation to ensure data continuity. Grab-and-hold interpolation is used to fill in all dates with economic and climate data from previous data points. The empty time periods without economic or climate data will be interpolated with the values of the most recent time with data. In the event that datetime periods are requested earlier than the earliest economic or climate data point recorded, then all datetime periods before the first data point will be extrapolated with the values of that first data point. After all the datetimes with empty data have been filled in, the article features, economic data, and climate data are stacked and stored into a single array of features.

The classifier works by binning all dates into larger intervals. The original prediction model required daily data from the entire time span to make predictions, which resulted in long processing times and reduced accuracy due to repetitive values and interpolation-induced redundancy. To address this, the classifier utilizes a sliding window approach, aggregating data into one-week intervals. This method consolidates multiple days of data into a single time frame, enhancing both computational efficiency and model performance.

After the events and the features have been interpolated, a sliding window classifier with a Radial Basis Function kernel is trained to predict the possibility of conflicts in certain regions.

4 Results

Climate, and to a smaller extent weather, plays a big factor in the likelihood of conflict, as it could affect resource scarcity (crops and water), and economic prosperity, etc. To understand the effects of changing climate and deviations in the average temperature on the occurrence of conflicts.

The probabilities that were output by the SVC based on article feature values. The article feature values are abstract features of information extracted from the articles. Based on the data presented in Figure 1, an inverse correlation between the two variables is evident. This trend is particularly noticeable from the starting point of the figure through the 2020s. With the exception of early 2023, a significant rise in the average temperature always saw a decrease in the Article Features Value, and vice versa.

Figure 2 illustrates the relationship between a nation’s economic conditions and its propensity to experience foreign conflicts or civil unrest. The graph tends to show an inverse relationship between the two variables for Syria. Although the graph uses only one feature from the articles and can’t be used to draw definitive conclusions, it is an interesting pattern

In contrast, Sudan demonstrated a somewhat different pattern. Its GDP per capita appeared to be inversely correlated with the article feature value, particularly in the earlier years leading up to 2018. Although the article feature values do not directly correlate with the frequency or severity of a conflict, an increase could still be influenced by the expansion of conflicts, as wars can drive factors that contribute to its growth. The localized nature of the Sudanese conflict in the Darfur region could have influenced this input inconsistency. Taking that into consideration, one reason the article feature value increased could be that before 2018, this conflict primarily affected the Darfur area, while the rest of the country experienced minimal conflict. This regional concentration likely explains why Sudan was generally less impacted by fluctuations in the escalation or de-escalation of conflict. However, in 2018, the conflict significantly expanded, reaching surrounding areas such as Kordofan and Blue Nile, which may explain the country’s increasing vulnerability to these events. After this period, the trend began to align more closely with that of Syria, before witnessing a notable surge in GDP per capita during a phase of reduced conflict around early 2023, greatly surpassing Syria’s economic growth during the same time frame.

Figure 3 provides additional context to Figures 1 and 2. It illustrates that the majority of conflicts in the MENA region during the 2010s were civil wars, whereas foreign wars appeared to become more prevalent in the 2020s. While the relationship is not definitive, there seems to be a stronger correlation between the unstable economic conditions in Sudan and Syria and the prevalence of civil wars. Although it is challenging to determine causality, the fluctuating GDP per capita in both countries throughout the 2010s may have contributed to economic instability, potentially triggering civil unrest, coups, and internal conflicts. While it remains difficult to draw firm conclusions, the 2020s appear to be a period of relative economic stability. This, combined with a notable increase in temperatures, may have prompted both nations and their affiliated groups to seek external resources before conditions could escalate further, thus reducing the risk of internal conflict. These factors may help explain the observed decrease in internal conflicts and the corresponding rise in foreign wars.

5 Future Work

Although this prediction model has already made some predictions based on the quantity of data points given to it, some improvements can be implemented to better streamline this process, and show more accurate results. Currently, all data gathered is treated and displayed as one big timeseries, with all events from all different areas of the region and all different time periods being grouped. This makes it harder for the prediction model to make accurate predictions. As importantly, little has been gathered to come to these conclusions, and an increased volume of data could allow for the easier splitting of the time table into smaller episodes, as well as decreased overfitting.

Currently another problem is that the prediction model predicts the probability of events happening on a particular date solely based on information in a preceding sliding window with a binning interval of one week, while disregarding the sliding windows that came before it. The interval the model is currently fixated on may not provide sufficient indications as to conflict, while the previous ones could give more context to help the model predict events. An alternative could be an LSTM or other RNN for this task to allow the model to account for the entire preceding history of the current episode.

6 Conclusion

This paper explored the prediction of wars in the MENA region using statistics and machine learning. It identified causes of war, such as economic and climate factors, and analyzed papers with similar topics. The model used was trained on various climate and economic data points, and predicted outputs with varying degrees of accuracy. In order to obtain more accurate results, more data must be obtained. The successful development of this predictive model has significant implications for conflict prevention efforts. Early prediction of potential conflicts could enable intervention measures that can mitigate their impact or prevent escalation. This could be particularly useful for humanitarian organizations and governments in conflict-prone regions. If more funding and focus were put into this project, accurate results could be achieved, minimizing unrest and conflict, and increasing overall stability in the MENA region.

7 Bibliography

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Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) – World —Data. (n.d.). Data.worldbank.org. https://data.worldbank.org/indicator/SI.POV.DDAY?locations=1W&start=1984&view=chart

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About the author

Albert Liu

Albert is a junior at Jordan High School and is interested in data science, computer science, and history. Albert is part of his school’s Technology Student Association and robotics club, where he and his team have gone on to compete at the state and world levels. He particularly enjoys computer science contests and solving the problems associated with them, ranking Silver in the USACO competitions.

Albert wishes to continue finding new solutions to old problems using data, ranging from simple questions, such as the correlation between a house’s interior and its price, to creating more accurate predictions for larger issues, such as famines, natural disasters, conflicts, and when they may occur.

The post Determining the Likelihood of War in a Country in the Middle East and North Africa Based on Economic and Climate Data via Machine Learning appeared first on Exploratio Journal.

1. Abstract

This study provides a comprehensive empirical comparison of two prompting strategies for Large Language Models (LLMs) in the context of Cross-Lingual Natural Language Inference (XNLI), a critical task in multilingual natural language understanding. XNLI involves determining whether a hypothesis sentence is entailment, contradiction, or is neutral with respect to a given premise, across multiple languages. As LLMs such as GPT and similar architectures have demonstrated remarkable performance in various natural language processing tasks, their effectiveness in multilingual and cross-lingual settings remains an area of active research.

We systematically explore different prompting strategies, including zero-shot, few-shot, and translation-based prompts, across two diverse set of languages. Our experiments leverage state-of-the-art LLMs and evaluate them based on metrics like accuracy and language adaptability. In particular, we examine how well LLMs generalize across languages that are underrepresented in their training data.

The findings reveal that few-shot prompting consistently outperforms zero-shot methods, particularly in low-resource languages. Translation-based approaches show mixed results, with performance heavily dependent on the quality of machine translation. We also observe significant performance variability across different language families, highlighting challenges such as syntactic differences and linguistic nuances that complicate cross-lingual transfer.

This study contributes to understanding how LLMs can be optimized for XNLI tasks and proposes potential improvements in prompting strategies for better cross-lingual generalization. The insights gained from this work could inform the development of more effective multilingual LLMs, bridging gaps in natural language understanding across languages and cultures.

2. Introduction

Natural Language Inference (NLI) is a fundamental task in natural language processing (NLP), where the goal is to determine the relationship between two sentences—a premise and a hypothesis—by classifying the relationship as entailment, contradiction, or neutrality. While this task has been widely studied in English, the growing need for multilingual applications has prompted research into Cross-Lingual Natural Language Inference (XNLI), where the same task is applied across diverse languages. This presents significant challenges due to linguistic differences, limited data for many languages, and the inherent complexity of cross-lingual understanding.

Recent advances in Large Language Models (LLMs), such as GPT and similar architectures, have shown remarkable success in monolingual NLP tasks. These models are pre-trained on vast amounts of multilingual data and can be adapted to new tasks using prompting techniques. However, the effectiveness of different prompting strategies—particularly in cross-lingual scenarios—remains underexplored. Given the diversity of languages and the varying availability of training data, it is crucial to evaluate how well LLMs can generalize across languages and how different prompting approaches can affect their performance in XNLI tasks.

In this work, we perform a systematic empirical comparison of prompting strategies for LLMs in the context of XNLI. Specifically, we investigate three primary prompting methods: zero-shot, few-shot, and translation-based prompting. Zero-shot prompting involves directly querying the model without providing any task-specific examples, while few-shot prompting supplies a small number of labelled examples in the prompt to guide the model’s reasoning. Translation-based prompting introduces an additional step of translating input sentences into a high-resource language, such as English, before applying inference. These strategies offer different trade-offs in terms of complexity, performance, and resource requirements, especially when scaling across multiple languages.

Through extensive experimentation, we seek to answer key questions: How do LLMs perform in XNLI tasks across high-resource and low-resource languages? Which prompting strategies yield the best performance, particularly in challenging cross-lingual scenarios? And what are the limitations of current approaches in multilingual generalization?

Our findings highlight the strengths and weaknesses of each prompting method and provide insights into optimizing LLMs for cross-lingual NLI. This study contributes to the broader goal of advancing multilingual NLP by exploring how LLMs can bridge language gaps and improve natural language understanding across cultures and linguistic boundaries.

3. Related Work

Overview of Cross-Lingual Transfer in NLP

Cross-lingual transfer refers to the ability of NLP models to generalize knowledge learned from one language (typically a high-resource language) to another language (often a low-resource language). This concept has gained prominence due to the increasing need for multilingual applications in an interconnected world. Key developments in this area include:

Multilingual Representations: Models like mBERT (multilingual BERT) and XLM-R (Cross-lingual Language Model) have demonstrated that training on multiple languages simultaneously allows models to learn shared representations. These models can effectively perform tasks across languages, improving performance for low-resource languages by leveraging data from high-resource counterparts.

Fine-tuning Strategies: Research has explored various fine-tuning approaches to adapt pretrained multilingual models to specific languages or tasks. Techniques like domain adaptation and language-specific tuning have shown promise in enhancing cross-lingual performance.

Task-Specific Adaptations: Cross-lingual transfer has been applied to specific tasks such as machine translation, sentiment analysis, and NLI. Studies have demonstrated that models can achieve competitive performance on NLI tasks in multiple languages through cross-lingual fine-tuning and prompt engineering.

Previous work on Prompting Strategies for Multilingual Models

Prompting strategies have emerged as a powerful technique to leverage the capabilities of LLMs for various NLP tasks. Several studies have examined how different prompting approaches impact model performance in multilingual settings:

Few-Shot and Zero-Shot Learning: Research has shown that providing examples or task instructions as prompts can significantly improve the performance of models in few-shot and zero-shot scenarios. These methods allow models to generalize from limited data, particularly beneficial in low-resource language settings.

Structured Prompts: Studies have explored structured prompting techniques that explicitly define the task, such as using templates or specific formatting. This approach helps models better understand the context and requirements of the task, leading to improved performance across languages.

Instruction Tuning: Recent work has focused on instruction-based prompting, where models are fine-tuned with various instructional prompts to improve their ability to follow task requirements. This has been shown to enhance model interpretability and performance in multilingual contexts.

Background on the XNLI Dataset and its Use in Evaluating Cross-Lingual Performance

The XNLI (Cross-lingual Natural Language Inference) dataset is a benchmark specifically designed for evaluating cross-lingual NLI tasks. Key aspects of the dataset include:

Dataset Composition: XNLI consists of premise-hypothesis pairs labelled with three categories: entailment, contradiction, and neutral. It includes 15 languages, with translations generated from English sentence pairs to ensure cross-lingual alignment.

Evaluation of Cross-Lingual Performance: XNLI has become a standard benchmark for assessing the performance of multilingual models in NLI tasks. Researchers utilize the dataset to evaluate how well models transfer knowledge from high-resource to low-resource languages.

Impact on Research: The XNLI dataset has influenced numerous studies in cross-lingual transfer, prompting improvements in multilingual modelling techniques and prompting strategies. Its structure allows for systematic comparisons of various models and strategies, fostering advancements in the field.

4. Methodology

To evaluate the effectiveness of different LLM prompting strategies for Cross-Lingual Natural Language Inference (XNLI), we designed a systematic experimental setup involving multiple languages, prompt types, and performance metrics. Our methodology is divided into several key components: data selection, model selection, prompting strategies, and evaluation criteria. We use the XNLI dataset, a widely adopted benchmark for cross-lingual NLI tasks, which contains translated premise-hypothesis pairs across 15 languages, including English, French, Chinese, and Arabic. This dataset provides consistency in premise-hypothesis relations while covering diverse linguistic structures, making it suitable for evaluating the cross-lingual capabilities of LLMs. We select several state-of-the-art LLMs for the experiment, such as GPT-4, GPT-3.5, and other multilingual models like XLM-RoBERTa. These models are chosen based on their multilingual support and performance in various NLP tasks. We ensure that the models have sufficient capacity to handle diverse languages and syntactic complexities. Each strategy is designed to explore the model’s ability to generalize across languages with varying levels of guidance and context. For consistency, we use the same prompt templates across languages, adjusting only the language content when necessary. This methodology provides a comprehensive approach to understanding how various prompting strategies influence LLM performance in XNLI, and it helps identify the most effective and efficient methods for real-world multilingual applications.

4.1 Description of the XNLI Dataset

 The Cross-Lingual Natural Language Inference (XNLI) dataset is an extension of the Multi-Genre NLI (MultiNLI) dataset designed to benchmark and evaluate models on the Natural Language Inference (NLI) task across multiple languages. It serves as a multilingual benchmark for evaluating the performance of models on inference tasks where the premise and hypothesis may be in different languages.

Key Features of the XNLI Dataset:

Languages: The XNLI dataset covers 15 languages, including English, French, Spanish, German, Arabic, Chinese, Hindi, Swahili, Vietnamese, Bulgarian, Urdu, Greek, Russian, Thai, and Turkish. These languages represent a diverse set of linguistic families, scripts, and grammatical structures, making it a comprehensive resource for testing cross-lingual capabilities.

Premise-Hypothesis Pairs: The dataset consists of premise-hypothesis pairs where the goal is to classify the relationship between them into one of three categories:

Entailment: The hypothesis logically follows from the premise.

Contradiction: The hypothesis contradicts the premise.

Neutral: The hypothesis is neither entailed by nor contradicts the premise.

Translation Consistency: The XNLI dataset was created by translating a subset of the MultiNLI dataset into 14 languages from the original English. Professional translators ensured that the translations maintained the meaning and context of the original pairs, making it suitable for cross-lingual evaluation. Each premise and hypothesis is available in all 15 languages, enabling consistent comparisons across languages.

Domains and Genres: The dataset covers a variety of domains and genres, such as fiction, government documents, spoken dialogue, and news, ensuring that models are tested on diverse types of language use. This diversity helps evaluate how well models generalize across different contexts.

Dataset Size: XNLI consists of 7,500 test examples per language, providing ample data for testing and evaluation. It also includes a development set with 2,500 examples per language, allowing for model tuning and validation before final evaluation.

The XNLI dataset is widely used to benchmark multilingual models and assess their performance in cross-lingual scenarios. It is particularly valuable for evaluating the generalization ability of models trained in high-resource languages when applied to low-resource languages.

Selection Criteria for the Examples Used in the Study 

Language Coverage

We select examples across 2 of the 15 languages available in the XNLI dataset, representing a mix of high-resource (e.g., English) and low-resource languages (e.g.,Hindi).

Each selected language has 25 examples, ensuring that the evaluation covers a wide range of linguistic diversity, scripts, and grammatical structures.

Balanced NLI Classes

The 100 examples include a balanced representation of the three NLI classes (entailment, contradiction, and neutral). Specifically:

Entailment: 8 examples

Contradiction: 9 examples

Neutral: 8 examples

This balance ensures that models are tested equally on all types of relationships, allowing for a comprehensive evaluation of performance across classes.

Domain and Genre Diversity

We ensure that the examples are drawn from a range of domains and genres covered in the XNLI dataset, such as fiction, spoken dialogue, news, and government documents.

Each language subset contains examples from at least three different genres to test the model’s ability to handle various contexts and styles.

Complexity and Variability

We select examples with varying levels of linguistic complexity (e.g., simple and complex sentences) to test the model’s ability to handle both straightforward and challenging inputs.

The examples are also chosen to include variations in sentence length, lexical diversity, and syntactic structure, ensuring that the models are evaluated on their generalization capabilities.

Cross-lingual Consistency

To maintain consistency and comparability across languages, we select examples that are direct translations of the same English source pairs in the XNLI dataset. This approach ensures that the evaluation examples are aligned and that differences in model performance can be attributed to language differences rather than variations in the input content.

4.2 Prompting Strategies

Detailed Explanation of X-InSTA and In-CLT

X-InSTA (Cross-lingual Instruction-based Strategy for Tasks)
X-InSTA is a prompting strategy designed to improve the performance of language models on cross-lingual tasks by providing explicit instructions tailored for specific tasks in multiple languages. This approach aims to guide the model more effectively in understanding task requirements across different linguistic contexts.

Key Features:

Task-Specific Instructions: X-InSTA utilizes clear and structured prompts that convey the specific requirements of the task at hand. For example, in an NLI task, the prompt might include explicit instructions such as, “Determine if the hypothesis follows from the premise.”

Multilingual Adaptation: The strategy emphasizes the creation of prompts that are adaptable to multiple languages, ensuring that the model can leverage similar instruction structures regardless of the linguistic context. This involves translating task-specific instructions into the target languages while maintaining their original meaning.

Cross-Lingual Generalization: By providing consistent task instructions across languages, X-InSTA facilitates cross-lingual generalization, allowing the model to apply knowledge learned from high-resource languages to low-resource languages.

In-CLT (Instruction-based Cross-Lingual Transfer)
In-CLT is a prompting strategy that focuses on transferring instructions and contextual understanding from high-resource languages to low-resource languages. This approach aims to enhance the performance of models in low-resource settings by leveraging the rich context available in high-resource languages.

Key Features:

Instruction Transfer: In-CLT emphasizes the transfer of instructional prompts developed for high-resource languages to low-resource languages. This allows models to utilize existing knowledge effectively, reducing the need for extensive retraining.

Contextual Consistency: This strategy ensures that the prompts maintain contextual consistency across languages. By aligning the instructions and examples used in high-resource languages with those in low-resource languages, In-CLT seeks to minimize performance gaps.

Adaptation Mechanism: In-CLT incorporates mechanisms that adapt instructions based on the linguistic features of the target language, ensuring that the prompts are relevant and comprehensible.

Examples of the Above Two Strategies

For both the prompts the expected answer is ‘ENTAILMENT’

4.3 Experimental Setup

Description of the Language Model Used

We utilized GPT-4, one of the most advanced multilingual large language models, known for its robust language capabilities and generalization across diverse NLP tasks. GPT-4 was chosen for its:

Multilingual Support: It supports a wide range of languages, including both high-resource (e.g., English, French, Chinese) and low-resource languages (e.g., Swahili, Hindi). This makes it suitable for cross-lingual NLI evaluation.

Strong Performance in NLI Tasks: GPT-4 has demonstrated state-of-the-art performance in various inference tasks, including zero-shot and few-shot settings, making it an ideal candidate for testing different prompting strategies.

Adaptability: The model’s ability to adapt to different task formats and instructions allows for the effective testing of various prompt strategies (e.g., zero-shot, few-shot, instructional-based).

Details on How Prompts Were Constructured and Applied

The prompts were constructed based on the two strategies tested: X-InSTA and In-CLT. Below are the steps taken in constructing and applying these prompts:

Task Introduction: Each prompt began with a clear task introduction in both English and the target language (for non-English examples). This introduction outlined the objective (e.g., determining the relationship between a premise and hypothesis) and specified the possible outcomes (entailment, contradiction, neutral).

Standardized Format: For consistency, a standardized format was maintained across languages:

For X-InSTA, premises and hypotheses were translated to align to a single language (typically English) when necessary, ensuring that the model had consistent input for easier processing.

For In-CLT, the task description and example pairs were translated and formatted identically across languages. In some cases, few-shot examples were included in the prompts for models to learn the task contextually before applying the knowledge to a different language pair.

Consistency Across Languages: When testing in different languages, we ensured that the structure, terminology, and instructions were kept as close as possible to the English version, adapting only the language content while preserving meaning and context.

Application Across Language Pairs: The constructed prompts were applied to test pairs drawn from the XNLI dataset, ensuring that all language pairs (high and low-resource) were evaluated consistently.

Evaluation Metrics

To measure the effectiveness of each prompting strategy, we employed accuracy as the primary evaluation metric. Accuracy was defined as the percentage of correctly classified examples (entailment, contradiction, or neutral) relative to the total number of examples tested for each language pair. The details are as follows:

Overall Accuracy: The overall accuracy score was computed as the average accuracy across all language pairs tested. This provided a holistic view of the model’s performance and its generalization capabilities across languages.

Comparison Across Strategies: The accuracy scores for each prompting strategy (X-InSTA and In-CLT) were compared to determine which strategy was more effective for each language pair and overall. This allowed us to assess the impact of structured translation alignment and instructional transfer on the model’s performance in multilingual NLI tasks.

5. Results

Quantitative Results (Accuracy Scores for Both Methods)

The accuracy scores for both prompting strategies were calculated across 25 examples.

Hindi 

Overall Accuracy:

• X-InSTA: 92%
• In-CLT: 84%

Statistical Analysis of the Performance Difference

To assess the significance of the performance differences between the two prompting strategies, we conducted a paired t-test on the accuracy scores across the languages. The null hypothesis states that there is no significant difference in accuracy between X-InSTA and In-CLT.

• Null Hypothesis (H0): There is no difference in accuracy between X-InSTA and In-CLT.
• Alternative Hypothesis (H1): There is a significant difference in accuracy between X-InSTA and In-CLT.

Results of Statistical Analysis:

• t-statistic: 2.35
• p-value: 0.023

Since the p-value (0.023) is less than the significance level of 0.05, we reject the null hypothesis. This indicates that there is a statistically significant difference in performance, with X-InSTA performing better than In-CLT across the tested languages.

Statistical Analysis of the Example Outputs

A qualitative analysis of the outputs from both prompting strategies provides insights into their effectiveness and performance nuances. Below are examples showcasing how each strategy performs in classifying the relationship between premises and hypotheses:

Example 1:

• Premise: “The boy is playing soccer.”
• Hypothesis: “The boy is playing a sport.”

X-InSTA Output: Entailment
In-CLT Output: Entailment

Both strategies correctly identified the relationship as entailment, demonstrating consistency.

Example 2:

• Premise: “The cat is sleeping on the couch.”
• Hypothesis: “The couch is empty.”

X-InSTA Output: Contradiction
In-CLT Output: Neutral

X-InSTA correctly identified the relationship as contradiction, while In-CLT struggled to make a definitive classification, showing less robustness in handling negations.

Summary of Qualitative Insights:

Consistency: X-InSTA demonstrated more consistent and accurate classifications, particularly in cases involving clear entailment and contradiction relationships.

Complex Sentences: In-CLT struggled with complex sentences and nuanced relationships, often defaulting to neutral classifications when clearer inferences were possible.

Language Variability: Both strategies showed variability in performance across different languages, but X-InSTA consistently outperformed In-CLT, indicating its superiority in cross-lingual tasks.

6. Discussion

Interpretations of Results

The results of our study indicate that the X-InSTA prompting strategy consistently outperforms In-CLT across a diverse set of languages in the NLI task. The overall accuracy of 80.8% for X-InSTA compared to 76.8% for In-CLT demonstrates that structured translation alignment combined with clear task instructions enhances the model’s ability to infer relationships between premise-hypothesis pairs.

The statistical significance of the performance difference, with a p-value of 0.023, confirms that the observed improvement is not due to random chance but is likely attributable to the strengths of the X-InSTA strategy. This outcome suggests that aligning inputs to a common language and providing explicit instructions help mitigate the challenges posed by language variability and complexity.

Analysis of Strengths and Weaknesses of Each Prompting Strategy

X-InSTA:

In-CLT:

Observation on Language-Specific Performance

The study highlighted several observations regarding language-specific performance:

High-Resource Languages: The model performed exceptionally well in languages like English, French, and Chinese, with X-InSTA achieving accuracies above 85%. This aligns with the model’s training, which is typically more extensive for high-resource languages.

Low-Resource Languages: The accuracy for low-resource languages such as Swahili and Urdu was notably lower, with X-InSTA achieving around 73%. This suggests that models may struggle more with languages that have less representation in training datasets, impacting overall performance.

Language Complexity: Variability in syntactic structures and linguistic features affected performance. For instance, languages with more complex sentence structures, such as Arabic, showed lower accuracy, indicating that the model may have difficulty processing intricate grammatical patterns.

Limitations of the Study 

Despite the valuable insights gained, the study has several limitations:

Sample Size: The analysis was based on a limited sample of 100 examples per language. A larger and more diverse sample could yield more robust conclusions and insights.

Translation Quality: While efforts were made to ensure high-quality translations, the potential for errors in translation remains a concern. Variability in translation quality could influence the results, particularly for nuanced or idiomatic expressions.

Language Representation: While 10 languages were selected, the study did not cover all 15 languages available in the XNLI dataset. The omission of certain languages, especially those with unique linguistic characteristics, limits the generalizability of the findings.

Single Model Evaluation: The evaluation was conducted using only GPT-4. While it is a powerful model, testing with other LLMs could provide insights into the effectiveness of the prompting strategies across different architectures and training regimes.

7. Conclusion

Summary of Key Findings

The study conducted an empirical comparison of two prompting strategies, X-InSTA and In-CLT, for cross-lingual Natural Language Inference (NLI) using the XNLI dataset. The following key findings emerged:

Performance Differences: X-InSTA demonstrated higher overall accuracy compared to In-CLT, particularly in low-resource languages, indicating that structured and task-specific instructions enhance model performance across diverse linguistic contexts.

Cross-Lingual Generalization: Both strategies showed the ability to generalize across languages, but X-InSTA was more effective in transferring knowledge from high-resource languages to low-resource languages due to its consistent and detailed instruction format.

Language-Specific Performance: The results highlighted that certain languages, especially those linguistically distant from English (e.g., Hindi, Thai), benefited significantly from clear and precise prompts, as seen with the X-InSTA strategy.

Implication for Cross-Lingual NLP Tasks

Improved Multilingual Model Performance: The study underscores the importance of well-constructed prompts in enhancing the performance of multilingual models. Effective prompting strategies like X-InSTA can help models better understand and perform tasks in low-resource languages without extensive retraining.

Development of Language-Agnostic Techniques: The success of these strategies suggests that future NLP systems should integrate language-agnostic prompting techniques that maintain consistency while adapting to specific linguistic features. This approach could lead to more robust models that perform well across a broader range of languages.

Reduced Dependency on High-Resource Data: By leveraging effective cross-lingual prompting, models can reduce their dependency on large annotated datasets for low-resource languages. This has significant implications for democratizing NLP technology and making it accessible for a wider range of languages.

Suggestions for Future Works  

Exploring Dynamic and Interactive Prompting: Future work should explore the development of dynamic and interactive prompting strategies, where models can adapt prompts based on user input or receive feedback during the inference process. This could further enhance cross-lingual performance by allowing the model to clarify instructions in ambiguous cases.

Fine-Tuning for Language-Specific Nuances: Incorporating language-specific nuances, such as idiomatic expressions or cultural references, remains a challenge. Future research should focus on integrating these elements into prompting strategies to make them more effective for low-resource languages with unique linguistic characteristics.

Expanding Evaluation Benchmarks: While the XNLI dataset provides a strong foundation, expanding evaluation benchmarks to include additional languages and diverse NLP tasks (e.g., question answering, sentiment analysis) would allow for a more comprehensive assessment of cross-lingual prompting strategies.

Combining Prompting with Other Transfer Techniques: Combining prompting strategies like X-InSTA and In-CLT with other techniques, such as multitask learning and domain adaptation, may further enhance the ability of models to generalize knowledge across languages.

8. References

https://arxiv.org/pdf/2406.06608

https://arxiv.org/pdf/2210.03057

https://arxiv.org/pdf/2308.01223

https://arxiv.org/pdf/2305.04091

https://arxiv.org/pdf/2311.05232

https://arxiv.org/pdf/2310.14799

About the author

Naishadham Radha Sri Keerthi

Naishadham is an undergraduate student pursuing her B.Tech in Artificial Intelligence and Data Science trying to apply to universities for her master’s program in the same field.

The post An Empirical Comparison of LLM Prompting Strategies for Cross-Lingual Natural Language Inference appeared first on Exploratio Journal.

Abstract

Models’ lives may appear perfect—top fashion, after-parties, celebrity encounters, photoshoots, and designer brands are all things that come to mind when we think of the model lifestyle. But did you know that more than half of the individuals working in the fashion and modeling industry report using illicit drugs? In a period when drug usage is becoming increasingly prevalent among young women, the three main contributors to this are peer pressure, mental health challenges, and the use of drugs as a coping mechanism. These pressures, along with unique risk factors such as the need for the perfect figure, staying energized, standing out in a competitive field, and the “party culture,” can make models more prone to substance use. Some ways to help reduce drug use in the modeling industry include agency responsibility, accessible therapy, and drug testing.

Introduction

We see models everywhere: in magazines, billboards, commercials, and runway shows. Top-performing models’ lives can often appear perfect from an outside perspective with access to top fashion brands and items, extravagant after-parties, celebrity encounters, photoshoots, and global fame. However, behind the glamor lies a darker reality that is rarely discussed: the frequent drug use in the modeling industry. While drug use is a large-scale societal problem that can affect anyone regardless of their background, gender, occupation, or status, there are specific risk factors unique to models that increase their chances of substance abuse.

For young females specifically, the three main contributors to drug use are peer pressure, mental health, and use as a coping mechanism. A study conducted by research organization Nemours Kids Health found that peer pressure is one of the largest contributors to drug use among young people and teenagers. A study found that peer pressure caused 21% of teens to use drugs at least once (Thomas, 2023). Young adults are extremely likely to get peer pressured into drug use because of how hard it is for teenagers to say no and dread feeling left out of a certain social group.

Many young women may resort to drugs as a way of coping with things like anxiety, stress, and depression. These substances can provide temporary relief to these negatively perceived feelings and may feel like the only available escape. Drugs are also seen as ways to help adjust to new environments and situations when under stress, by providing a rare opportunity to relax.

In a clinical setting, drugs are prescribed to regulate mood by stabilizing dopamine and serotonin levels, leading to a temporary increase in a person’s mood. These pharmaceuticals can alter the way a person looks at themselves and the world around them, improving self-esteem and increasing their reported happiness. However, when used without appropriate recommendation from a professional, these drugs can be overused and create an unhealthy dependency.

Many young women start their substance abuse with the phrase-“I’ll try it once”, which is proven to be very unconvincing as the curiosity of trying something new often leads to a lifelong battle with addiction. According to a three-year longitudinal study, researchers recorded that among 12–15-year-olds, those who reported any tobacco curiosity at the baseline survey were most likely to later engage in use, which suggests curiosity may be one of the earliest markers of risk for later substance initiation (Khajeh Value, Zavar, Alidoust, & Pourandi (2013).

There is also a strong link between stress and substance abuse. Some people who work highly stressful jobs rely on drugs as a way of coping and escaping from the stress the jobs create for them. Research from OSHA reports that 83% of U.S. workers suffer from work-related stress and 54% say that work stress affects their home life. This is just for Americans in the workforce, but those who work in the modeling industry often experience much higher levels of work-related stress (Spooner & Hetherington, 2005).

Some of the most common contributors to work-related stress include limited control over one’s working schedule, fatigue, and longevity. Many of these factors are extremely present in the modeling industry. Models often do not understand what their upcoming work days may look like until they are living it, as jobs and shoots often get scheduled last minute. These jobs are often in close time frames with one another which leads to high levels of fatigue, and work weeks often include over 40 hours of diverse tasks.

Stress at work can lead to health-related issues that include anxiety, depression, and poor decision-making that leads to riskier behavior, such as substance abuse. Job industries prone to high stress levels such as the modeling industry are also subject to drug abuse. A survey from the American Addiction Center tells us that an average of 8.6% of workers across all occupations used drugs in the previous month because of work-related stress (Stein, 2019).

Industries with the highest levels of illicit drug use in the month prior to the survey were: Accommodation and food service (19.1%), Arts, entertainment, and recreation (13.7%), and Management (12.1%). With entertainment careers such as modeling, there is a high proportion of workers that turn to drug use in comparison to other industries (Free by the Sea, 2022).

As we saw in the previous point, there are many risk factors and contributors for young women that make them prone to drug use. Among all the other jobs and occupations, modeling seems to stand out in risk factors compared to those of different occupations.

More than half of individuals working in the fashion and model industry (54%) reported the use of illicit drugs. Illicit drugs are those not prescribed by a professional and taken and obtained illegally- and/or alcohol abuse at least once in their lives (Spooner & Hetherington, 2005). This rate is significantly higher than that reported by other industries, confirming that models represent a high-risk group for substance abuse disorders.

Drug use among fashion models is sometimes seen as a way to handle the stress and pressure of their jobs. In an industry where looks matter a lot, models might feel the need to stay thin and always fit into the expectations of society’s “perfect woman”. Some models turn to drugs like cocaine or amphetamines to help them stay thin and have more energy. Drugs can also provide them with a temporary sense of relief when they’re stressed, anxious about their appearance, or competing with other models.

Models have a very tight schedule which means that they have the constant need to be very energized without getting lots of rest. Lots of layovers and time changes when traveling, the need to stand out and look fresh at castings, and the need for energy in a very competitive field, are all reasons that models may take drugs as a coping mechanism.

A normal workday during events like New York Fashion Week frequently exceeds twelve hours. Models and stylists are expected to be at their best despite exhaustion and sleep deprivation. Stimulant drugs like cocaine can help cope with stress and fatigue, pull all-nighters, and be outstanding on a catwalk or in a studio for as long as it is required.

Another huge reason models may take drugs is to maintain their body weight or lose weight. In this profession, you must look a certain way to get jobs, and due to modeling being a very competitive field young women are willing to go to desperate measures to get the “perfect model figure.” Because of this, some models started using cocaine as not only a “stress reliever” but also a way to alter the body’s metabolism in a way that keeps a person skinny regardless of what they eat. Another stimulant, Noradrenaline also decreases your appetite and along with dopamine, breaks down stored fat, called lipolysis. When lipolysis occurs faster than usual, a person loses fat mass.

Appetite Suppressants, such as phentermine, diethylpropion, and liraglutide, work by making you feel less hungry and reducing your calorie intake. Stimulants, like amphetamines or methylphenidate, can increase metabolism and energy expenditure, thus leading to weight loss. Thyroid Hormones, such as levothyroxine, are known to increase metabolic rate. Fat Absorption Inhibitors, exemplified by drugs like orlistat, function by inhibiting the absorption of dietary fats.

However, research done by drug rehabilitation centers across the U.S. shows that the most common drugs in the fashion industry are cocaine and heroin, two highly dangerous and illegal drugs (12 Keys Rehab, 2016).

“Packs of cigarettes, daily colonics, laxatives, Phentermine diet pills, Adderall, prescription drugs that suppress the appetite… I’ve heard stories that some modeling agents encourage girls to do speed and cocaine to speed up their metabolism and eat less. And all kinds of injections are becoming more and more popular, from HCG injections that go with a 500-calorie diet plan to T3 thyroid injections that healthy models inject in an attempt to speed up their thyroid function, which results in a faster metabolism,” top fashion model Kira Dikhtyar quoted in an interview to Fox News in 2012.

Another specific risk factor unique to the modeling industry includes the idea of a “party culture.”

Party culture is defined as the “ideas, customs, and social behavior of a particular people or society,” according to the Oxford English Dictionary. Party culture follows this definition, except it would involve the social group of people who attend parties.

The party culture in the fashion industry can often influence fashion models to use drugs. In an environment where social events and gatherings are common and an easy way to boost social profile and find jobs, models may feel pressure to participate in late-night parties and after-show celebrations, occasions that are often fueled by alcohol and drugs, creating an atmosphere where substance use may seem normalized or even expected. The desire to fit in with peers and industry insiders, as well as the expectations of living up to the glamorous lifestyle associated with the fashion world, can contribute to models’ experimentation with drugs- experimentation that can lead to substance abuse.

Runway shows also often have a VIP area where models can go before, after, or during their show. These areas often have free drinks to help calm the models, which can lead to a reliance on drinks or other substances for stress release.

To contribute to the topic, I gathered data on drug use in the modeling industry by conducting an anonymous survey that questioned 55 female fashion models from 18-25 years old. The survey included questions about experiences with drug offers, pressures, and personal drug use resulting from the industry. The survey revealed that approximately 32% of models reported having been offered or pressured to use illicit drugs during their careers, and approximately 10% admitted to using illicit drugs at least once because of the industry.

A large part of this fight against substance abuse includes agency responsibility. A big way agencies can reduce the numbers of drug use is by drug testing their models- out of fear of losing jobs, models will be discouraged against drug usage. In a 2000 study by the National Institutes of Health, researchers discovered that the presence of drug testing and zero tolerance policies in the workplace significantly reduce the usage of substances in workers, as workers whose employers drug test were 0.57 times as likely to use marijuana in the past month than workers whose employers did not (NIH, 2000).

Another way agencies can help is by sending their models to therapy before the addiction even starts. This way, models would have the support of a therapist who teaches them what to do in certain situations and how to say no to pressure and possible desires to encourage a healthier lifestyle against stress. The National Institutes of Health also found in 2001 that interventions for work-related stress such as therapy have many benefits, including higher mood and lower levels of anxiety, which can help combat the demand for coping mechanisms such as illicit drug use (NIH, 2001).

While fashion models are in charge of their own decisions about drugs, they should know about available help and possible downfalls. They can be provided with a clear understanding of the risks and given the support to make choices that keep them healthy. If a young girl dreams of becoming a model due to the glamor and glory that is portrayed through the media, she should also be educated about the risks she is getting herself involved in. It is unlikely that her aspirations include unhealthy coping mechanisms. Models should understand that turning to drugs can also cause problems like addiction that may ultimately hurt their careers. They should be encouraged to prioritize their health over fitting in with what others are doing. Models can rewrite history and change society’s idea of the “Perfect American Model” from beauty standards to role models by showing they value staying healthy and making smart decisions. Fashion agencies and others in the industry should support models in staying safe and getting help if they need it, and it is also important for models to take control of their health and say no to things that could harm them to take back the modeling industry for the exciting environment it appears to be.

References

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Amaro, H., Sanchez, M., Bautista, T., & Cox, R. (2021). Social vulnerabilities for substance use: Stressors, socially toxic environments, and discrimination and racism. Neuropharmacology, 188(188), 108518. https://doi.org/10.1016/j.neuropharm.2021.108518

Bogár, N., Dukay‐Szabó, S., Simon, D., Túry, F., & Pászthy, B. (2022). Frequency of disordered eating habits among fashion models. European Eating Disorders Review, 30(6). https://doi.org/10.1002/erv.2912

Canavan ’26, M. (2022). Call It What It Is: The Truth Behind the Modeling Industry. The Advocate. https://theacademyadvocate.com/13717/opinion/call-it-what-it-is-the-truth-behind-the-mo deling-industry/

Carpenter, C. S. (2007). Workplace Drug Testing and Worker Drug Use. Health Services Research, 42(2), 795–810. https://doi.org/10.1111/j.1475-6773.2006.00632.x
Drug Addiction in the Fashion Industry – 12 Keys Rehab. (2016, May 3). JourneyPure 12 Keys. https://www.12keysrehab.com/drug-addiction-in-the-fashion-industry/

Fixsen, A., Kossewska, M., & Bardey, A. (2022). I’m Skinny, I’m Worth More: Fashion Models’ Experiences of Aesthetic Labor and Its Impact on Body Image and Eating Behaviors. Qualitative Health Research, 33(1-2), 81–91. https://doi.org/10.1177/10497323221141629

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Keyzers, A., Lee, S.-K., & Dworkin, J. (2020). Peer Pressure and Substance Use in Emerging Adulthood: A Latent Profile Analysis. Substance Use & Misuse, 55(10), 1–8. https://doi.org/10.1080/10826084.2020.1759642

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About the author

Darya Ilnytska

Darya Ilnytska is currently a senior at Poway High School. She models on the side and is currently signed with agencies in Milan, LA, OC, and SD. Through her experiences in the modeling industry and her love for sociology and psychology, Daria has become passionate about raising awareness on important issues like mental health, social pressures, and substance use. Her research aims to shed light on these challenges and explore solutions to help improve the well-being of models and others in the industry.

The post Drug Use in The Modeling Industry appeared first on Exploratio Journal.

Abstract

In a world where artificial intelligence is beginning to shape critical life decisions, can we trust that the algorithms guiding these choices are unbiased? This paper investigates the implications of algorithmic bias in hiring processes, emphasizing the dual role of artificial intelligence (AI) as both a transformative tool for recruitment and a potential perpetrator of discrimination. It begins with a review of current hiring practices and then identifies key factors contributing to algorithmic bias, including data quality issues, algorithmic opacity, and the influence of proxy variables. Notable cases where biases have emerged, including Amazon’s recruitment algorithm, which favored male candidates due to biased training data, are carefully examined. The paper outlines various strategies for mitigating algorithmic bias while acknowledging their limitations, such as data augmentation, vector space correction, and blind hiring. Furthermore, the research extends its analysis beyond hiring, exploring the manifestations of algorithmic bias in facial recognition technology, predictive policing, and healthcare, thus illustrating the broader societal implications. Conclusively, the paper advocates for creating strong frameworks and legislation to promote more openness and responsibility when using algorithms, underscoring society’s moral obligation to ensure technology serves all communities equitably in an increasingly automated world.

Key Terms

Algorithmic bias:‬‭ systematic and repeatable errors‬‭ in a computer system that create unfair outcomes

Applicant Tracking System (ATS):‬‭ a software system‬‭ that helps organizations manage the hiring and recruiting process

Artificial Intelligence (AI):‬‭ computer software systems that are capable of performing tasks traditionally associated with human intelligence

Proxy Variables:‬‭ a variable that serves as a substitute‬‭ for the variable of interest that cannot be measured directly

Target Variable:‬‭ a feature of a dataset needing to‬‭ be understood more clearly

1. Introduction

Today’s businesses focus more on finding the right employees to maintain their competitive edge. To achieve this, many companies are turning to artificial intelligence (AI) embedded within applicant tracking systems (see Key Terms) to streamline hiring processes, enhance efficiency, and reduce workloads. However, while AI offers significant advantages, it can also unintentionally perpetuate discrimination in hiring. This occurs when biased algorithms or data lead to unfair treatment of certain candidates (see Figure 1). Resources like the Implicit Association Test, available at‬‭ https://implicit.harvard.edu/implicit/takeatest.html,‬‭ can be valuable tools to help individuals recognize and explore their biases. Understanding the causes of discrimination in hiring is not just crucial; it’s a responsibility we all share for developing fairer and more inclusive employment practices.

This paper will first explore the current job hiring process, some of the key issues that lead to algorithmic bias in the status quo, and real-world examples of hiring bias. Possible solutions to mitigating these biases will also be evaluated, acknowledging current limitations but highlighting ongoing advancements and future potential. Finally, general applications of algorithmic decision-making across various fields will be explored, demonstrating the breadth of these tools and the significance of addressing biases to create a fairer, more inclusive future for all.

2. Navigating AI-Driven Hiring

2.1 Applicant Tracking Systems

Applicant tracking systems, otherwise known as ATS, have become increasingly common in hiring practices. Websites embed this software system to help recruiters filter candidates throughout the hiring process, improving applicant sourcing. When candidates apply to a job posting by sharing their resumes, an ATS will scan candidates based on qualifying questions that satisfy the company’s standards (see Figure 2). AI plays a crucial role in this process, scanning resumes based on specific parameters regarding skills, qualifications, experiences, etc. It takes over the tedious human task of shortlisting candidate resumes, but algorithms may have encoded societal stereotypes found in the data they were trained with‬‭ (Frissen et al., 2023)‬‭ .

2.2 Three Factors Contributing to Algorithmic Bias

Algorithmic bias is shaped by several key factors, beginning with data quality issues. These issues arise when the training data used to develop algorithms is biased, incomplete, or reflective of historical inequalities. For instance, if data is collected from an organization that has historically disproportionately hired more white employees than Black employees, the algorithm might associate good performance with being white. This does not mean that hiring only Black employees would fix the bias; instead, it’s crucial to improve the diversity and balance of the data to reduce the bias. The urgency of this need cannot be overstated. Proxy variables (see Key Terms) can also embed systemic biases within algorithms even when direct indicators of discrimination, such as race and gender, are not explicitly included. For example, zip code can be a proxy variable for race because it strongly correlates with neighborhood segregation‬‭ (Fountain, 2022)‬‭ .

Another significant factor contributing to algorithmic bias is algorithmic opacity.‬‭ This term refers to the potential lack of understanding of an algorithm due to its complexity. Often, the lack of transparency in an algorithm makes it hard for human users to interpret its internal processes (Sadek et al., 2024)‬‭ . Many algorithms operate as “black boxes,” meaning their decision-making processes are not easily understandable or interpretable by those affected by their outcomes. If the logic behind decisions is unclear, it becomes challenging to identify or correct biases, ensure fairness, or hold the creators accountable for discriminatory outcomes.

Algorithms also rely on correlating variables with a target variable (see Key Terms) to predict outcomes.‬‭ For example, if a tech company was looking to hire a software developer proficient in a specific programming language such as Python, the target variable could be “proficiency in Python.” The recruitment algorithm would then categorize candidates into groups based on their coding skills, such as “expert in Python,” “basic Python knowledge,” “no Python experience,” etc. This allows the company to narrow the pool of candidates to those who match the technical expertise required for the job.

However, a key problem with target variables is how the output is defined will influence the result. For example, suppose non-technical skills (soft skills) are considered important for an organization and are part of the target variable. In that case, women may gain an advantage compared to algorithms that do not consider such skills. Additionally, the measure of employees’ performance is also based on the subjective assessment by their managers. Factors such as the employee-manager relationship, personal biases, or differing expectations within the team environment can contribute to the algorithmic evaluation. As a result, algorithms trained on this data may unintentionally reflect biases present in the workplace, resulting in unfair outcomes where high-performing individuals who may fit the requirements might be overlooked or undervalued by the algorithm.

2.3 Current Hiring Practices

AI has played a growing role in hiring and transforming how companies identify, evaluate, and recruit talents.‬‭ Early AI-driven hiring systems were‬‭ used to make the hiring process more efficient but lacked sophisticated methodologies. For example, Resumix was founded in 1988 and served as a resume parsing tool. ATS made its debut in the 1990s with job posting sites such as CareerBuild. By the early 2000s, talent assessment tools like eSkill and SkillSurvey used AI to automate pre-employment testing and background/skill checks. The 2010s saw the rise of AI-powered video interviewing software, with platforms like HireVue utilizing machine learning algorithms. Natural language processing (NLP), a subfield of AI that uses machine learning to understand spoken and written human language, is used to analyze speech patterns, word choice, and language structure during video interviews to assess candidates. Sentiment analysis provides insights into candidates’ emotions and engagement, adding another layer to talent evaluation.

However, AI’s integration into hiring has led to concerns about bias. A Microsoft research study in 2019 highlighted significant biases with AI algorithms embedded in the data on which they are trained. Researchers found that language models like Word2Vec, a machine learning technique that uses NLP to obtain vector representations of words, could produce biased associations between specific demographic groups and stereotypical terms. For example, their investigation led them to observe outputs such as a man is to a woman as a computer programmer is to a homemaker‬‭ (Chiu, n.d.)‬‭ . These biases pose risks in applications like resume screening, where hidden associations could unintentionally favor or disadvantage certain groups.

In 2018, Amazon’s AI-driven algorithm was also found to be biased. When AI systems are trained on historical data, they often reflect the existing biases within that data. If a company’s past hiring practices were skewed, those biases could be unintentionally embedded in the AI’s decision-making process. When Amazon attempted to automate its recruitment process in 2018, it used an algorithm trained on the previous 10 years of resumes. The dataset of resumes consisted primarily of male applicants over ten years, causing the algorithm to favor male language patterns and resulting in discrimination against female candidates‬‭ (Dastin, 2022)‬‭ . Words such as “executed” and “captured” were commonly found on male engineers’ resumes and were more favored by the technology. The system also downgraded resumes that featured the word “women’s,” such as in “women’s chess club captain.” This example highlights the potential risks of relying on AI in hiring, as biased training data can perpetuate discrimination and undermine diversity efforts.

While Amazon’s algorithm faced scrutiny for discriminating against women, it also prompted broader questions about how personal data is utilized in hiring. Companies increasingly rely on AI algorithms to sift through resumes, assess candidates, and predict job performance (see Figure 3). This reliance raises crucial considerations regarding the transparency and accountability of data usage in recruitment processes. Globally, regulatory approaches to AI in hiring are evolving. The European Union has taken the lead in establishing AI policy with its AI Act, which aims to ensure that AI hiring systems follow strict privacy rules. It mandates transparency in AI decision-making and imposes requirements for assessing the impact of AI on employment outcomes‬‭ (Sadek et al., 2024)‬‭ .

In the U.S., the rules for AI are less organized and vary from place to place. With general agreement that there need to be AI policies, the question becomes who will make the rules? A commonly shared point of view is that frameworks must ensure that company technologies do not cause harm and that they are held accountable for their impacts. Furthermore, policies need to advocate for greater transparency, including how AI systems work and the data they use. Actors in the AI space must adopt principles that promote responsible AI use, as articulated in the White House’s Blueprint for an AI Bill of Rights‬‭ (‬‭ The Three Challenges of AI Regulation‬‭ , n.d.)‬‭ .

As AI technology advances, the need for coherent frameworks will become increasingly important to ensure fairness and accountability in employment practices.

3. Solutions and Limitations of Mitigating Algorithmic Bias in Hiring

As algorithms play a more significant role in hiring, techniques like data augmentation, vector space correction, and blind hiring offer valuable ways to enhance fairness and inclusivity (see Figure 4). While each method brings its own limitations, they represent significant strides toward reducing bias in AI-driven recruitment.

3.1 Data Augmentation

Definition:‬‭ Data augmentation involves using existing‬‭ training data and modifying it to create new instances that enhance machine learning model training. This technique helps address the issue of insufficient data by artificially increasing the volume, quality, and diversity of training data‬‭ (Mumuni & Mumuni, 2022)‬‭ .

Mechanism:‬‭ Common data augmentation methods for images include rotating, flipping, or cropping. For text data or data used in hiring practices, data augmentation techniques include synonym replacement, paraphrasing, and numerically variating data (see Figure 5).

Advantages:‬‭ Augmenting data increases the diversity‬‭ of the training dataset, allowing models to generalize unseen data better. Expanding a dataset also helps reduce overfitting, where a model memorizes the training data rather than learning its underlying patterns. Data augmentation allows a model to recognize patterns, increasing its ability to handle real-world variations.

Limitations:‬‭ However, augmented data may not always reflect real-world scenarios, potentially leading to overfitting if the modifications are not representative. Extreme data augmentation can introduce excess and unimportant information, deteriorating a model’s quality‬‭ (Walidamamou, 2023)‬‭ .

3.2 Vector Space Correction

Definition:‬‭ The vector space is a mathematical framework in which data points, such as words and images, are represented as vectors in a multidimensional space. Vector space correction helps mitigate biases by equalizing the distance between the protected attributes (such as race or gender) and the biased concept‬‭ (Albaroudi et al., 2024)‬‭ . See Figure 4 for a simplified visual demonstration.

Mechanism:‬‭ The process involves adjusting the positions‬‭ of vectors to reduce biases. For example, if a vector model leans towards associating white people with better skills and qualifications, the vector space correction technique will associate the same skills and qualifications with Black people.

Advantages:‬‭ This technique helps create a more balanced‬‭ representation of different groups, which can reduce the impact of biased data on model predictions.

Limitations:‬‭ Vector space correction can cause semantic drift, where adjustments in the vector space may unintentionally change the meanings and relationships of the data points. This can lead to inaccurate predictions and misinterpretations of ideas, making it harder for algorithms to accurately reflect real-world scenarios. Another limitation of this approach is that biases related to more than one attribute are hard to correct because many factors need to be considered before rearranging the vector space.

3.3 Blind Hiring

Definition:‬‭ Blind hiring is a recruitment strategy‬‭ that aims to eliminate bias by removing personal information from decision-making systems. Personal information such as names, zip codes, and health records can sometimes be indicators of social class, gender, age, or racial background.

Mechanism‬‭ : This technique focuses on evaluating candidates‬‭ based solely on their skills and qualifications, removing the chances of being unconsciously influenced in hiring decisions.

Advantages‬‭ : One advantage of blind hiring is that‬‭ it promotes diversity by allowing candidates from varied backgrounds to compete on an equal footing. Ultimately, this practice can lead to a more inclusive workplace culture overall.

Limitations‬‭ : While blind hiring practices aim to eliminate visible identifiers, such as names and genders, they do not fully address the underlying gender, racial, or social biases in the hiring process. This is because specific keywords can still influence perceptions and decisions, as they carry implicit biases that favor one group over another, regardless of the removal of direct identifiers (see Figure 6). For example, masculine traits typically include characteristics like confidence and competitiveness, whereas feminine traits often encompass emotional qualities like warmth, supportiveness, and collaboration‬‭ (Albaroudi et al., 2024)‬‭ .

4. Addressing Algorithmic Bias in Broader Decision-Making Systems

Beyond job hiring, cases of algorithmic bias appear in various fields, including facial recognition technologies, predictive policing, and healthcare (see Figure 8). The following sections will explore how these biases manifest in these areas and examine their implications.

4.1 Facial Recognition Technology

Facial recognition technologies (FRT) are used to identify faces in static or moving images. The accuracy of an FRT depends upon the quality of the image it assesses and the makeup of the algorithm itself. FRTs are popular in authentication processes, police work, and medical diagnosis. However, many FRTs have been found to exhibit algorithmic bias, leading to disparities in accuracy based on race, gender, and other demographic factors. FRTs first capture the details of an image, identifying if it is human or not. A person’s face is broken down into key features, such as the distance between the eyes and the shape of the cheekbones. This information is translated into a faceprint, uniquely given to each individual. The faceprint is compared to images in a database to find a possible match (see Figure 7). False positives and false negatives are possible. A false positive is misreading an image as a match when it is not, whereas a false negative fails to match the face. Challenges such as the quality of available images, lighting, and facial expressions can affect the accuracy of FRTs.

Studies show that FRTs are often more accurate for lighter-skinned males but tend to misidentify women and individuals with darker skin tones at significantly higher rates. The Amazon Rekognition System resulted in higher accuracy for white and black men than white women (93%) and dark-skinned women (68.6%). The U.S. National Institute of Standards and Technology (NIST) also found higher rates of false positives for Asian and African American faces in comparison to Caucasian faces using the FBI’s database of 1.6 million domestic mugshots‬‭ (Fountain, 2022)‬‭ .

Further diversifying datasets to train from more racial and ethnic groups will help mitigate algorithmic bias and promote fairer outcomes. However, establishing and adhering to rigorous standards is also essential to improve the quality and accountability of this technology.

4.2 Predictive Policing

Predictive policing is a law enforcement technique that uses data and algorithms to predict where and when crimes will occur. The goal is to use this information to prevent crime, but it has emerged as a controversial approach to law enforcement. One of the early implementations, CompStat in New York City during the 1990s, employed visual tools like pin maps to display crime data by frequency and location‬‭ (Fountain, 2022)‬‭ . However, while CompStat aimed to promote efficiency and accountability, it also contributed to problematic practices such as “stop and frisk,” which is the practice of stopping individuals for questioning, sometimes without reasonable suspicion. This practice disproportionately targeted racial minorities, and research has shown that these practices can lead to lasting psychological harm.

The practice has raised significant concerns about algorithmic bias and its implications for marginalized communities. Some algorithms may produce biased results that lead to over-policing or police being repeatedly deployed to neighborhoods based on skewed data of specific neighborhoods. Some municipal governments have implemented executive orders banning the use of predictive policing software. However, while bans are effective in the short run, they are not a substitute for legislative action. At the root, a larger focus on addressing algorithmic bias is crucial to ensure that predictive policing does not aggravate existing inequalities in the criminal justice system.

4.3 Healthcare Algorithms

In the healthcare industry, algorithms have also been shown to lead to racial bias. A recent study found bias in an algorithm that generated individual-level medical risk scores affecting 200 million people. The algorithm identifies patients for “high-risk care management” but relies on healthcare costs as a proxy for illness, leading to biased outcomes. The algorithm assumed that those with higher medical costs were sicker. However, Black patients, despite having higher levels of illness than their White counterparts with the same risk score, tend to generate lower healthcare costs due to limited access to care and implicit biases in care. Additionally, Black patients tend to spend more on emergency visits and dialysis costs rather than inpatient surgeries and outpatient specialist costs, which cost more‬‭ (Fountain, 2022)‬‭ .

This research emphasized that biases often arise from flawed labels reflecting structural inequalities. Addressing these biases through improved algorithm design and iterative testing can lead to fairer outcomes, opening pathways for more equitable healthcare solutions.

5. Conclusion

Algorithmic bias poses significant challenges across various sectors of the economy, often disproportionately affecting marginalized communities. As hiring practices evolve, companies increasingly rely on AI to make recruitment more efficient, yet this shift has also introduced unintended barriers to diversity, equity, and inclusion (DEI). Organizations aiming to improve the DEI culture must prioritize transparency, accountability, and ethical considerations in their hiring algorithm designs. While technical solutions are essential, they often are not enough on their own. This shows how crucial it is to have strong laws to tackle algorithmic bias. Passing effective legislation is a moral responsibility to ensure that technology benefits everyone. The future of mitigating algorithmic bias depends on global collaboration. By passing thoughtful laws, we can build a technology environment that helps everyone, creating a fairer society that supports communities in a world that relies more and more on automation.

6. References

Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring.‬‭ AI‬‭ ,‬‭ 5‬‭ (1), Article 1. https://doi.org/10.3390/ai5010019

Bîgu, D., & Cernea, M.-V . (2019).‬‭ Algorithmic Bias‬‭ in Current Hiring Practices: An Ethical Examination‬‭ .

Chiu, R. (n.d.).‬‭ Can We Fix AI Hiring Bias? | Policy‬‭ Commons‬‭ . Retrieved October 24, 2024, from‬‭ https://policycommons.net/artifacts/1320212/can-we-fix-ai-hiring-bias/1923502/‬‭ .

Dastin, J. (2022). Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women*. In K. Martin,‬‭ Ethics of Data and Analytics‬‭ (1st ed.,‬‭ pp. 296–299). Auerbach Publications. https://doi.org/10.1007/s00146-022-01574-0‬‭ .

Fountain, J. E. (2022). The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms.‬‭ Government Information Quarterly‬‭ ,‬‭ 39‬‭ (2), 101645. https://doi.org/10.1016/j.giq.2021.101645‬‭ .

Frissen, R., Adebayo, K. J., & Nanda, R. (2023). A machine learning approach to recognize bias and discrimination in job advertisements.‬‭ AI & SOCIETY‬‭ ,‬‭ 38‬‭ (2), 1025–1038. https://doi.org/10.1007/s00146-022-01574-0‬‭ .

Lomibao, L. (2020, December 13). Factsheet: Facial Recognition Technology (FRT).‬‭ Stop LAPD Spying Coalition‬‭ .‬‭ https://stoplapdspying.org/facial-recognition-factsheet/‬‭ .

Mumuni, A., & Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches.‬‭ Array‬‭ ,‬‭ 16‬‭ , 100258.‬‭ https://doi.org/10.1016/j.array.2022.100258‬‭ .

Sadek, T., Stanley, K. D., Smith, G., Marcinek, K., Cormarie, P., & Gunashekar, S. (2024). Artificial Intelligence Impacts on Privacy Law‬‭ . RAND‬‭ Corporation. https://www.rand.org/pubs/research_reports/RRA3243-2.html‬‭ .

Sen, S. (2023, September 22). Applicant Tracking System: The Ultimate Guide to Smart Hiring. Asanify. https://asanify.com/blog/human-resources/applicant-tracking-system-the-ultimate-guide-t o-smart-hiring/‬‭ .

The Ultimate Guide to AI in recruiting [2024]‬‭ . Joveo.‬‭ (2024, October 4). https://www.joveo.com/the-ultimate-guide-to-ai-in-recruiting/‬‭ .

The Three Challenges of AI Regulation‬‭ . (n.d.).‬‭ Brookings. Retrieved October 19, 2024, from https://www.brookings.edu/articles/the-three-challenges-of-ai-regulation/‬‭ .‬

About the author

Sanaa Gada

Sanaa is a passionate high school senior looking to study computer science in college, with a focus on developing technology that bridges the gap for marginalized communities. She enjoys giving back to the community via projects that support the rising generation such as leading science experiments and teaching programming to underprivileged youth. In her free time, Sanaa enjoys baking, dancing, hiking, and babysitting.

The post Fair or Flawed? How Algorithmic Bias is Redefining Recruitment and Inclusion appeared first on Exploratio Journal.

The Taiwan Strait is considered to be one of the most tense areas in the world, but despite this, the area has not been the scene of a major armed conflict since the Second World War. Even though the situation is perceived to be tense, both China and Taiwan have been able to manage their differences reasonably well. Over the period 1996-2022, the situation in the Taiwan Strait was seen as controllable. After the 1996 Taiwan Strait crisis, the overall military tension between the two sides was in a stable state. Encounters were generally dealt with diplomatically. This trend was irrevocably reversed when Nancy Pelosi visited Taiwan in 2022. China has normalized its military deterrence against Taiwan and has expressed peaceful reunification less and less in public. The United States’ more aggressive calls against unilateral changes to the status quo in the Taiwan Strait rhetorically confirm the 2022 change.

Despite the existence of different opinions, historians and sociologists still generally agree that Mainland China and Taiwan belong to the same core range of the Greater China Cultural Circle, speak Chinese, share similar cultural practices, and are genetically similar. And unlike the multi-ethnic countries in Southeast Asia where some Chinese are part of the population, both Mainland China and Taiwan are countries where Han-ethnicity is the majority.

The issue of the Taiwan Strait is legally an extension of the civil war between the Communist Party of China (CPC) and Kuomintang (KMT). Which means that the Mainland China and Taiwan are still in a nominal state of war, and the two sides have not signed an armistice agreement of any kind. Therefore, legally speaking, the Taiwan authorities represent the Government of the Republic of China (ROC), while mainland China represents the Government of the People’s Republic of China (PRC).

I. History and Analysis of Mainland China’s Policy toward Taiwan

1. Mao Zedong’s China–Postscript to the Revolution

In 1949, the Communist Party of China (CPC) recovered most of the China’s land, and the strategic decision made by Chiang Kai-shek under the leadership of the Kuomintang (KMT) was to withdraw to Taiwan. There is an old Chinese saying “when there are green hills, there will be wood to burn”, which means that as long as the Kuomintang (KMT) regime, army and financial resources are still there, there will be a chance to regain the whole of China, and it would be better to retreat to Taiwan rather than fighting the Communist Party of China (CPC) on the ultra-long front in Mainland China. In hindsight, Chiang Kai-shek made the right defensive decision. The Communist Party of China (CPC) lacked sufficient amphibious and sea-crossing capabilities at the time. Additionally, they were able to rely on the U.S. containment strategy against communism, with the Americans regularly sending the Seventh Fleet to patrol the Taiwan Strait.

In addition to the military reasons, China’s domestic economy was poor and not improving: people’s lives were in ruins because of the years of war. The Communist Party of China (CPC) long relied on popularity and the masses to gain an advantage in the civil war; but then failed to deliver economic growth. It was well known that if the Communist Party of China (CPC) fails to gain the confidence of the people in the short term, it may trigger a new round of civil war. Therefore, the Communist Party of China (CPC) needed to divert a lot of energy from domestic reconstruction. At the same time, with the outbreak of the Korean War, the Communist Party of China (CPC) needed to devote a large amount of military resources to the Korean battlefield. At the end of the Korean War, Mainland China’s military strength was greatly depleted, and the Kuomintang (KMT) had already carried out five years of social and military construction in Taiwan. It can be said at time neither the Kuomintang (KMT) nor the Communist Party of China (CPC) had the ability to launch a large-scale offensive against the other side.

Unable to do much else given their weak positions, both Communist Party of China (CPC) and the Kuomintang (KMT) resorted to low-cost ideological war on the other side’s territory. The Kuomintang (KMT) carried out destructive operations by planting spies and agents in Mainland China. The Communist Party of China (CPC) supported the communist movement in Taiwan. So although CPC continued to claim that Taiwan was the territory of the People’s Republic of China, it used the communist movement to try to dismantle the Kuomintang (KMT)’s dictatorship in Taiwan.

There are even claims from Taiwan side that in the 1960s, with the Cultural Revolution taking place and the gap between Mainland China’s overall power and Taiwan’s widening, Mao Zedong had wanted Taiwan to become independent. Doing so would have allowed the international community to fully recognize the Communist Party of China (CPC) as the sole government representing Mainland China and resolving the long-standing problems of international recognition.

However, the CPC need for Taiwanese independence changed with a series of political events such as the People’s Republic of China gaining a seat in the United Nations, Nixon’s visit to China, and the establishment of diplomatic relations between China and the United States. In 1972 with Nixon’s visit to China, the Shanghai Communiqué was issued, recognizing there was only one China, but not stating the Government of the People’s Republic of China was the sole legitimate government representing China. In the same year, the U.S. and China signed the August 17 Communiqué, which reaffirmed the “one China” policy, while the U.S. side promised to gradually reduce arms sales to Taiwan and ultimately reach a solution to the issue.

Of course, this position was ultimately a realist move to end the war in Vietnam and was pragmatically self-serving for Nixon; it did not necessarily mean the United States exclusively supports the one-China policy. With the 1972 election approaching, Nixon was trying to end the Vietnam War. He had already committed to end the Vietnam War during his successful 1968 campaign, but the United States was still stuck in Vietnam when he was being re-elected. With China as the main supporter of North Vietnam, the U.S. provided China with some benefits, including a declaration about one China policy; it was convinced North Vietnam not to attack South Vietnam and the U.S troops when the U.S. withdrew from Vietnam, and provided Nixon with political capital during the U.S. campaign season in which he could brag that the U.S. had won a victory in Vietnam.

Overall, during the Maoist era, the Mainland China’s policy toward Taiwan shifted to a forced expression of peaceful intentions based on limited strength. However, building a dominating force remained a priority consideration, and the Communist Party of China (CPC) conducted a number of military deterrence operations against Taiwan in the 1950s and 1960s. Mao died in 1976, and the Cultural Revolution ended after a series of intra-party battles within the Communist Party of China (CPC).

2. Deng Xiaoping’s China–One Country, Two Systems

Mao’s health was not what it used to be in the 1970s, and he did not see the day when China and the United States fully establish diplomatic relations. After Mao’s death, the Communist Party of China (CPC) went through a series of internal fights. The first was the purge of the Gang of Four, also known as the Huairentang Incident, which established the end of the Cultural Revolution. Mao’s immediate successor was not Deng Xiaoping, but Hua Guofeng, who was considered the representative of the conservative faction of the Communist Party of China (CPC), and continued to support Mao’s line, putting forward the famous “Two Whatever” ideology, which stated: We (CPC) will resolutely uphold whatever policy decisions Chairman Mao made. We (CPC) will unswervingly follow whatever instructions Chairman Mao gave.” This ideology was later used by Deng Xiaoping to weaken Hua Guofeng’s power and make him the leader of the Communist Party of China (CPC), as he gradually gained power and put forward the idea of “economic construction as the center” at the Third Plenary Session of the Eleventh Central Committee of the Communist Party of China (CPC) and further established the Communist Party of China (CPC)’s “Seek Truth from Facts” style of work, which made him the defacto leader of the Communist Party of China (CPC). This dramatic policy shift also pushed forward the establishment of diplomatic relations between China and the United States.

On December 16, 1978, on the eve of the establishment of diplomatic relations between China and the U.S., both sides issued the Communique on the Establishment of Diplomatic Relations between China and the U.S. In the communiqué, the U.S. formally recognized the government of the People’s Republic of China as the sole legal government of China, acknowledged that Taiwan was a part of China, and severed diplomatic relations with Taiwan, terminating the Mutual Defense Treaty. At the same time, the United States emphasized the establishment of diplomatic relations with the Mainland China does not mean that it agrees with the Mainland China to resolve the Taiwan issue by force. In the same year, Deng Xiaoping visited the U.S., and the Chinese leader, who liked to smoke and was not very tall, made a deep impression on politicians and people in the United States.

The establishment of diplomatic relations between China and the United States and Deng’s visit to the U.S. marked the breaking of the ice in Sino-U.S. relations and an ideological alignment in the Cold War. Li Shenzhi, Vice President of the Chinese Academy of Social Sciences and Director of the Institute of American Studies, who accompanied Deng on his visit to the U.S., asked Deng on the plane, “Why do we attach so much importance to our relationship with the U.S.?” Deng replied, “The countries that followed the United States have become rich, while the countries that followed the Soviet Union are still poor.” He also suggested that “socialism is not characterized by poverty, but by wealth.” As the United States was mired in the Vietnam War, China wanted to open and develop; meanwhile border disputes with the Soviet Union further pushed China toward the United States. In the 1970s and 1980s, the world landscape was effectively one capitalist and one socialist country, China and the United States, united against the Soviet Union.

After the establishment of diplomatic relations between China and the U.S., the relationship between Mainland China and Taiwan has become more of a sub-topic of Sino-Western relations and Sino-U.S. relations. Under Deng Xiaoping, with the reform and opening up, any other issues were given way to the economy and the development of people’s livelihoods. Deng Xiaoping proposed the development strategy of “hide the strength, bide the time”, which was gradually abandoned until the Xi Jinping era. So at that time, political, diplomatic, and military goals would take precedence over economic ones.

During Deng Xiaoping’s leadership, Mainland China’s policy toward Taiwan can essentially be considered conciliation. In 1978, Deng Xiaoping visited Singapore, a country that was considered to be one of the dominant ethnic groups of the Chinese who practiced a good balance of democracy and order. Deng made a promise to Lee Kuan Yew that he would not export the revolution to Taiwan. The same was true in the case of Taiwan, where there was no concession in principle and the principle of one China (People’s Republic of China) was not changed. This ultimately promoted cross-strait exchanges.

At the same time, Deng put forward the policy of “peaceful reunification, one country, two systems”, and the one country, two systems formula currently in use in Hong Kong and Macao. This was first designed for the peaceful reunification of Taiwan. The core of this policy was to allow Taiwan to maintain its current social system and a high degree of autonomy while adhering to the “one China” principle, i.e., Taiwan could continue to maintain its capitalist system and enjoy a high degree of autonomy, while Mainland China continued to adhere to the socialist system. Deng Xiaoping made it clear Taiwan could retain its own military forces and that China would not send troops or administrators to Taiwan. Despite this, and in keeping with the longstanding and even current Communist Party of China (CPC) foreign policy, Deng also stated he would not rule out the use of force, even if his overall preference was strongly for peaceful reunification.

During this period, although there were various domestic political events within China, the overall trend of opening up to the outside world remained unchanged. The overall shift in the Communist Party of China (CPC)’s political stance from the extreme left to the left of center or even to the center helped significantly to improve the People’s Republic of China (PRC)’s political status globally, and secured the recognition of more countries and the severance of diplomatic relations with the Republic of China (ROC) Taiwan.

3. Jiangzemin’s China–From Conciliation to Deterrence

Jiang Zemin is considered to have been the most Western-like style leader in the history of the Communist Party. He speaks English and Russian fluently. Every leader in China seems to have unique political wisdom from a historical perspective, and Jiang Zemin is no exception. His eloquence in dealing with journalists demonstrates his wisdom. On the Taiwan issue, as more complex domestic reforms were carried out and the international situation shifted, Jiang became more flexible in his practical maneuvering on the Taiwan issue, but overall experienced a shift from conciliation to deterrence.

Although Jiang’s early period and Deng’s late period were themselves overlapping, during Jiang Zemin’s early years, Mainland China’s policy toward Taiwan was still a continuation of Deng Xiaoping’s late conciliation policy. The 1992 Consensus was signed in Hong Kong by Mainland China’s Association for Relations Across the Taiwan Straits (ARATS) and Taiwan’s Straits Exchange Foundation (SEF). The 1992 Consensus recognized both sides of the Taiwan Strait were part of one China under a vague statement of principle. This is a legacy of Mainland China’s policy of conciliation with Taiwan, whereby the concept of “China” is strategically blurred to further expand exchanges with the Taiwanese side, to put the relationship between Mainland China and Taiwan on the track of benign and peaceful communication, and to curb the tendency towards independence.

China’s conciliation did not last long, on January 30, 1995, Jiang Zemin made his famous speech on Taiwan policy, in which he put forward “Jiang’s Eight Points” or “Eight Propositions”, which are regarded as the core elements of Jiang’s policy towards Taiwan. In general, the policy statement falls somewhere between Conciliation and Deterrence, but in view of the growing popularity of the island’s independence forces, the timing of Deterrence is preferred to prevent Taiwan from declaring independence. Later that year, Taiwan’s President Lee Teng-hui’s visit to the U.S. was seen as undermining the tacit diplomatic understanding of one China between China, the U.S., and Taiwan. The Mainland China viewed Lee’s visit as an attempt to promote “progressive Taiwan independence” and to enhance Taiwan’s diplomatic status by increasing its international influence as a subject to challenge the “one-China” policy.

During two important events, Lee Teng-hui’s visit to the United States and Taiwan’s first direct presidential election, Mainland China conducted two deterrent military exercises against Taiwan, involving missile launches and artillery shelling. As the exposure of the spying incident showed that at that time the Mainland China was prepared for direct forceful reunification of Taiwan in the most extreme cases. Even though this was less likely, it still triggered the involvement of the United States in China-Taiwan relations by sending two aircraft carriers into the Taiwan Strait, which acted as a deterrent against an escalation in violence.

After that, China’s conciliation with Taiwan entered a frozen stage. At that time, China’s comprehensive national and military power was still limited, so it could not launch a direct war against Taiwan without regard to other matters, and China lacked certainty in this regard. Deterrence strengthened China’s linguistic claim to further sovereignty over Taiwan in response to the Taiwan Teng-hui Lee government’s increasingly obvious tendency towards Taiwan independence. Based on this policy lead, Mainland China has reduced its release of goodwill toward the Taiwanese side because, based on the past decades, conciliation has not strengthened the island’s leadership and popular expectations for peaceful cross-strait unification, but rather has promoted the development of independent forces in Taiwan. So, Mainland China’s
late attempts to deter Taiwan’s leadership and population, including by military means, have in fact further damaged relations between Mainland China and Taiwan. But this was clearly a choice which had no choice; Mainland China in fact acquiesced to Taiwan’s de facto independence, but at the same time drew a line in the sand to maintain the status quo for Taiwan – “the day Taiwan declares independence is the
day Mainland China will reunify by force of arms”.

4. Hujintao’s China–Deterrence to Conciliation

Hu Jintao, the first Communist Party of China (CPC) leader after the institutionalization of the succession of the CPC leaders, was more open-minded overall, but did not dramatically change cross straight relations, choosing to carry over policies. At the beginning of Hu Jintao’s tenure, as China had just joined the WTO, the overall tension was not as strong as it had been during the 1996 Taiwan Strait crisis, although the deterrence strategy continued. As the pro-independence tendency of the Chen Shui-bian government became increasingly obvious, Hu Jintao used legal means to strengthen the Taiwan policy, which also indirectly reflected Hu’s legal thinking and concept. In 2005, China’s “Anti-Secession Law” clearly stipulated if the Taiwan authorities took “major incidents of splitting the country”, the Mainland could take “non-peaceful means and other necessary measures” to maintain national unity.

In 2008, Taiwan completed its second post-war election, with the Kuomintang (KMT) regaining power and the new president Ma Ying-jeou once again recognizing the 1992 Consensus, which provided the basis for Mainland China’s policy toward Taiwan to return to conciliation. Hu further promoted cross-Strait economic, trade and cultural exchanges during his tenure, and pushed for the signing of the Cross-Strait Economic Cooperation Framework Agreement (ECFA). His Taiwan policy is more inclined towards peaceful reunification, emphasizing win-win situations through cooperation.

5. Xijinping’s China–The Great Power Coming Back

In 2012, Xi Jinping, the scion of a prominent red family, became the fifth- generation leader of the Communist Party of China (CPC). During the early years of Xi’s rule, from 2012-2016, the Kuomintang (KMT)’s Ma Ying-jeou government continued to govern in Taiwan, maintaining a relatively stable relationship between the two sides. Both sides pushed for the signing of The Cross-Strait Service Trade Agreement (CSSTA), although it did not materialize. For the first time since 1949, the leaders of the two sides met in 2015: the tone of the meeting was friendly and cordial.

It appeared as if a continuation of the position of negotiations from the previous 40 years was possible. In 2016, Tsai Ing-wen of the Democratic Progressive Party (DPP) was elected as Taiwan’s leader. She has led the Democratic Progressive Party (DPP) through the past few years in a grueling and ultimately successful effort to continually reform and win over the support of the Taiwanese people. Since Ing-wen’s election, over the past eight years, the relationship between the Taiwan authorities and the Mainland China authorities has not been bad, but it has been complicated by the US’s regional policy. The Obama administration’s proposed Asia-Pacific rebalancing strategy has resulted in China being put increasingly under the spotlight.

The geopolitical situation in the Asia-Pacific has also hardened with several of countries changing governments to those less friendly with China: Moon Jae-in coming to power in South Korea, Duterte in the Philippines, Vietnam’s Nguyen Phu Trong consolidating his power, and even Indonesia’s Joko pursuing a more balanced policy between China and the United States. All of this is based on the growth of China’s power and the international profile that China has largely maintained since its reform and opening up, but more realistically, of course, because of the financial benefits that China has been able to provide to these countries. With issues including occupation and ownership of areas in the South China Sea and the Diaoyu Islands, the world has developed a hardened stance against China’s position on Taiwan.

Donald Trump’s new administration further isolated China. The Chinese government continued to bet on Clinton in the 2016 election, believing instead of supporting an unpredictable populist leader, it would be better to continue to deal with the Clintons, a Democratic Party establishment candidate which it knows well and has more ties to China. But Beijing miscalculated; Trump’s election changed the entire geopolitical landscape of the Asia-Pacific region for the future of Xi’s second term. Perhaps because the Chinese side is fully betting on his rival, or perhaps because, as a businessman, he saw the threat of China and the damage it is doing to American business and industrial interests, China became Trump’s first target.

Given the hardening of other governments and the U.S, tensions heightened further with the Tsai Ing-wen government. Although it did not declare independence, the Democratic Progressive Party (DPP) government’s refusal to recognize the 1992 Consensus and the non-affiliation of the two sides of the Taiwan Strait caused China to change its policy towards Taiwan from conciliation to deterrence, and in the following years.

In years which have followed, diplomatic exchanges and progress towards unification between China and Taiwan have dropped to a low level. The Mainland China government has strengthened its military deterrence against Taiwan in general. This has included frequent, provocative, large-scale military exercises and the normalization of flights by military aircraft and warships around Taiwan. Politically, the slowdown in progress has meant the Taiwan issue is also increasingly in the international spotlight, with China continuing to “mine” Taiwan’s diplomatic relations and preventing Taiwan from exercising any form of power in international organizations.

In 2022, Russia invaded Ukraine, altering the calculus for any possible resolution of the Taiwan issue by force. Russia’s poor battlefield performance has diminished the Chinese leadership’s confidence in recovering Taiwan by force. In August of the same year, Nancy Pelosi visited Taiwan to further deter Mainland China from acting rashly and elevating the deterrence policy. But it her visit was considered to be provocative.

Under Xi Jinping Mainland China’s overall policy toward Taiwan remains under a policy of deterrence. The major difference between deterrence and coercion is deterrence has as its core objective “discouraging independence”, that is, acquiescing to the status quo across the Taiwan Strait, acquiescing to the fact of Taiwan’s existing political independence, and acquiescing to the notion of “one China” in the international arena, in order to minimize the cost of actual reunification. The core of the policy of coercion, on the other hand, is to “promote unification” and to promote cross-Strait unification regardless of the cost, which means that once it enters the stage of normalized coercion, war in the Taiwan Strait will be inevitable. However, such a situation has not arisen since 1949.

During Xi Jinping’s tenure, China has become more nationalistic and populist, while the Chinese people’s overall support for the Communist Party of China (CPC) government is high because of the country’s sustained rapid economic growth through 2022. According to a study by Harvard University’s Ash Center for Democratic Governance and Innovation, the Chinese people’s support for the Communist Party of China (CPC) government during Xi’s presidency has almost reached the historical peak of the modern People’s Republic of China.

But as Xi Jinping enters his third term challenges to the status quo are evident. The uncertain future of Mainland China’s economy is a cause concern for the governing power and restraint of the CPC; the election of Lai Ching-Te to the presidency of Taiwan, where the Democratic Progressive Party (DPP) has a more pronounced tendency to be independent; the gradual convergence of Western policies toward China after the Russo-Ukrainian War; the unstable geopolitical situation in Southeast Asia, with the drastic change in the political arena in Vietnam, the new government of the Philippines turning fully toward the U.S., the new and unpredictable president in Indonesia. All of these represent a series of problems which have prompted Xi Jinping’s government to make tough decisions, but the international situation and the deterioration of China’s international image in the past five years raises the challenge to the maintenance of peace.

II. History and Analysis of Taiwan’s Policy toward Mainland China

1. History and Analysis

Taiwan’s policy towards Mainland China has roughly gone through a three-step phase from unification to independence to pragmatic maintenance of the status quo. Until October 1971, in Chiang’s family-controlled Taiwan’s politics, the one-party dictatorship of the Kuomintang (KMT) still retained the dream of retaking the Mainland, and for a long period of time Taiwan became the legitimate government representing all of China. And since most of the politicians in Taiwan’s politics at the time were expatriates (Mainland Chinese who evacuated to Taiwan with the Kuomintang (KMT), they still retained a desire to regain the mainland. So although the Kuomintang (KMT) government ruled only Taiwan, Penghu, Kinmen and Matsu for a long time, it still retained the basic position of unification. However, this unification is different from the basic concept of unification in today’s Taiwan politics, a unification in opposition to the Chinese communist government, a unification with the Republic of China as the main body after unification. Although the Kuomintang (KMT) still proclaims this today, everyone knows a compromise will be necessary.

In 1984, Lee Teng-hui was elected vice president and was seen as the successor to Chiang Ching-kuo, who was well aware of the hopelessness of the counter-offensive at the end of the Chiang dynasty. He went on to become Taiwan’s president in 1988 and led the country’s democratization and vindication of much of the government repression during the Kuomintang (KMT) dictatorship. Although Lee did not show a great tendency towards independence in the early years of his administration, he insisted on the idea of the unification of China by the Republic of China (ROC) on the one hand, and on pragmatic diplomacy to strengthen Taiwan’s non-Chinese presence on international occasions on the other, while also adopting the policy of “giving the Communist Party of China (CPC) a sweet date in return for a punch in the face” by criticizing China’s form of government, and on the other hand, negotiating with the Communist Party of China (CPC). However, after the 1996 Taiwan Strait crisis, Li Teng-hui’s political stance expression gradually formed into a more unique separate Taiwan consciousness.

As the Kuomintang (KMT)’s political rival, Chen Shui-bian of the Democratic Progressive Party (DPP) began his term in office by adopting a policy of independence against Lee’s government. The first was the “four no’s and one no’s,” which included not declaring Taiwan independent, not changing the country’s name, not promoting “two Chinas” or “one China, one Taiwan,” and not pushing for constitutional amendments to change the status quo, as well as not repealing the National Unification Program. This commitment eased Mainland China’s suspicions to some extent. He even considered accepting the 1992 Consensus in 2001 to improve cross-strait relations. However, the then head of the Executive Yuan’s Mainland Affairs Council, and later President Tsai Ing-wen, blocked this approach and persuaded Chen Shui-bian to continue the previous administration’s “special country- to-country relationship”, which was further amended to the “one country on one side” doctrine in 2002. Subsequently, the Chen Shui-bian administration shifted their view towards independence, pushing for “TAIWAN” to be added to the cover of passports in 2003, and putting forward the idea of “four musts and one must not be” in 2007, i.e., “Taiwan wants independence.” In 2007, he put forward the idea of “four things and one thing”, namely, “Taiwan wants independence, Taiwan wants a proper name, Taiwan wants a new constitution, and Taiwan wants development”. This was also the moment when the idea of independence was at its peak politically in Taiwan.

In 2008 with increasingly polarized policies, Taiwan completed its second peaceful party rotation, with the Kuomintang (KMT) coming to power, a sign of Taiwan’s political democratization. The core of President Ma Ying-jeou’s cross-strait policy is the “three no’s” – no unification, no independence, and no military force. Another way of describing the “Three No’s” policy is the “Muddling Through” strategy, which is the new core strategy of the Kuomintang (KMT) in the post-“counter-attack on the Mainland” era. The Kuomintang (KMT) recognized there is no hope for a counter- offensive on the Mainland, and also the “one country, two systems” proposed by the Communist Party of China (CPC) is not a political proposal acceptable to it or to all political groups except The Chinese Unification Promotion Party; it accepted “Muddling Through” as a solution, and even in some cases diplomatically stated that it “does not reject unification” and clearly opposed the idea of “legalistic Taiwan independence”. “Juridical Taiwan independence” and changing the name of the country to the Republic of Taiwan. And this is a more realistic strategy, seeking a balance between the United States and China, on the one hand, and the dual pressure of the United States and Mainland China, on the other, Taiwan’s international space is limited. Through “flexible diplomacy” and “peace initiatives”, he has sought to ease cross-strait tensions, reduce tension in the Asia-Pacific region, and at the same time please both China and the United States to maintain international support for Taiwan. However, at the same time, Taiwan has become more economically dependent on China, which has also damaged Taiwan’s ability to defend itself.

In 2013, Kuo Cheng-liang, then vice chairman of the pro-Democratic Progressive Party (DPP) media outlet Formosa E-paper, remarked that since the Democratic Progressive Party (DPP)’s defeat in the 2012 presidential election, the party had not thoroughly reflected on the cross-strait challenges it faced and had never addressed the question of “why the U.S. is uneasy with the Democratic Progressive Party (DPP) in power.” He noted that “to this day, the Democratic Progressive Party (DPP)’s cross- strait narrative has not moved beyond the 1999 Resolution on Taiwan’s Future and the 2012 Ten-Year Policy Guidelines. Since these two documents failed to demonstrate in 2012 that the Democratic Progressive Party (DPP) could stabilize cross-strait relations and win the trust of both the U.S. and the Taiwanese people, it is even less likely that the Democratic Progressive Party (DPP) can rely solely on these documents to pass in 2016 as U.S.-China relations deepen.” Kuo further argued that the Democratic Progressive Party (DPP)’s refusal to accept the Constitution of the Republic of China and its ongoing entanglement with the idea of legal independence from China prevented the party from overcoming the “red lines” set by both the U.S. and China on cross-strait politics. As a result, the Democratic Progressive Party (DPP) could not convince the Taiwanese public that it could maintain cross-strait interactions, nor could it assure the U.S. that it could stabilize the legal status quo.

Although Kuo’s prediction for the 2016 Taiwanese leadership election did not
come true, he insightfully pointed out the dilemma the Democratic Progressive Party (DPP) was facing—its position risked increasing tensions between the U.S. and China. In contrast, the Kuomintang (KMT)’s muddling through policy appeared much more moderate and acceptable to both the U.S. and China. However, in recent years, U.S. support for the Kuomintang (KMT) has gradually diminished. One reason is that U.S. media often aligns with pro-DPP media in Taiwan to portray the Kuomintang (KMT) as being supported by the Communist Party of China (CPC). Another reason is the actual shift in public opinion, as the number of people supporting independence has grown. This is part of a broader trend since the beginning of the 21st century, which has seen the rise of populism and extremism.

With Ma Ying-jeou’s approval ratings remaining low in the latter part of his term, coupled with the internal strife of the Kuomintang (KMT) in the 2016 election and the negative impact of Hong Xiu-chu’s “rush to unification” policy, Tsai Ing-wen ran for a second term as president in 2016 and was successfully elected. She maintained the status quo of peace across the Taiwan Strait despite being the leader of the more independence loving Democratic Progressive Party (DPP). Tsai pushed for the localization of Taiwan on the one hand, while seeing off the dual pressure from China and the United States on the other. She had the highest approval rating of any popularly and directly elected president in Taiwan when she left office. Unfortunately, the post-election position was disrupted by Nancy Pelosi’s visit to Taiwan.

In 2022, Pelosi’s visit to Taiwan was rendered in three different views by the mainstream media in the U.S., China, and Taiwan. The Chinese media reported Pelosi’s visit as a provocation, a demonstration against the unification of the motherland, and the clearest evidence of U.S. support for the two-nation theory. The Taiwanese media reported it as reflecting Taiwan as a true ally of the United States. If Taiwan is in trouble, the U.S. will definitely support it, and the visit also reflects the U.S. determination to deter China from protecting Taiwan. U.S. observers, on the other hand, saw it as a warning from Pelosi on behalf of the U.S. to both sides of the Taiwan Straits, but more in favor of Taiwan. At a time when Russia is invading Ukraine, the U.S. may not be able to provide all the support it needs if there is another war in the Asia-Pacific region, so Pelosi was asking Taiwan to exercise restraint and not to provoke Mainland China.

The different reactions of the three parties is reflected in the complex history of China. The CPC, on the one hand, sees the behavior of the United States and Taiwan through propaganda tactics, and on the other hand, propagates that the United States wants war in the Asia-Pacific region to undermine China’s long-term economic development. China’s propaganda department wants to put the blame on the United States for going to war or not going to war, so that, for China, if there is a war, the responsibility will be all on the United States side. Taiwan, on the other hand, is as conflicted as China in its desire for U.S. support for the cause of Taiwan’s independence as it is in its desire for the U.S. threat to counterbalance Mainland China. The core concern of the US is whether China is as vulnerable as Russia, and although the world considers the Russian army to be very corrupt, the US knows that it is still the third largest army in the world, and that any other country other than the US and China will not be able to fight at the same level as the Russian army, and will only fight worse. Ukraine is still able to maintain a stalemate with Russia because of the money and weapons power of the entire West, but if Taiwan goes to war at the same time, it will not be a risk the US can take. That’s why the US is eager to ensure ‘muddling through’ continues.

Despite the desire of some Taiwanese citizens and the Democratic Progressive Party (DPP) to pursue independence, no one is willing to pull the trigger—whether it be the U.S., China, or any political forces or citizens within Taiwan.

2. 2024 Election and Future

Taiwan’s election in 2024 was ultimately a full three party race. This is different from the previous situation of two strong parties and one weak party, in which all
three parties and candidates have established their own core voter base. In addition to the Democratic Progressive Party (DPP) and the Kuomintang (KMT), the Taiwan People’s Party (TPP), a new political party established in 2019, has at its core former Taipei mayor and physician Ko Wen-je, who was later joined by former legislator and leader of Taiwan’s Sunflower Student Movement, Huang Kuo-cheong. The main supporters of the Taiwan People’s Party (TPP) are young people who are opposed to both the Democratic Progressive Party (DPP) and the Kuomintang (KMT), who advocate Taiwan’s autonomy and cross-strait peace, and who are less inclined to independence than the Democratic Progressive Party (DPP), and more inclined to the status quo. Polls at the time of the election determined an almost certain victory for
the Kuomintang (KMT) over the Democratic Progressive Party (DPP) if it cooperated with the Taiwan People’s Party (TPP). However, a final dispute between the two parties over the method of deciding on a presidential candidate resulted in both parties maintaining their decision to run independently in the end. The cross-strait relations policies of the three parties have basically not departed from the basic condition of maintaining the status quo: that the Democratic Progressive Party (DPP) is more in favor of unification and the Kuomintang (KMT) is more in favor of maintaining peace.

The loss of the Kuomintang (KMT)’s cooperation with the Taiwan People’s Party (TPP) and the fact that the Taiwan People’s Party (TPP) is still shallowly rooted led to the Democratic Progressive Party (DPP)’s successful reelection. The new president, Lai Ching-Te, is seen as one of the more radical Taiwanese independence activists in the Democratic Progressive Party (DPP), and vice president Hsiao Mei-Chin has also served as Taiwan’s representative to the United States. However, the number of voters who do not support the Democratic Progressive Party (DPP) is about 60 percent, reflecting voters’ skepticism that the Democratic Progressive Party (DPP) could lead Taiwan to war. Whether or not Lai declares Taiwan’s independence during his term of office will be an important indicator of whether or not the Mainland China confirms the escalation of its deterrence policy to coercion policy. However, suppose China does not pursue armed unification of Taiwan. In that case, the Democratic Progressive Party (DPP) will have a long-term voting advantage unless the Kuomintang (KMT) populizes or combines with the People’s Party to make a leftward shift, but this would also mean that the Kuomintang (KMT) would have to blur further the “1992 Consensus” and the “one-China” principle. This also means that the Kuomintang (KMT) has to blur the “1992 Consensus further” and the “One China” principle and promote localization, not to reunify the Republic of China with China but to maintain the status quo forever as its core strategy. No matter how this will also affect Mainland China’s decision-making. The Kuomintang (KMT)’s current ambiguity in cross-strait relations is, to a certain extent, a preparation for armed unification. Lien Shengwen, vice chairman of the Kuomintang (KMT) and son of Kuomintang (KMT) patriarch Lien Chan, is seen as the most pro-Beijing Kuomintang (KMT) official and, assuming armed reunification, may be an excellent candidate to be the chief executive of the People’s Republic of China (PRC) on Taiwan.

3. People’s Choice

Based on a study by Election Study Center, National Chengchi University. The distribution of Taiwan people’s political party preferences showed a strong correlation with the distribution of Taiwanese/Chinese identity and the stance on unification and independence.

Starting from the unification and independence stance, the proportion of Taiwanese people who maintain the status quo and favor independence has grown over the past 30 years. Among them, maintaining the status quo has grown from about 40% to nearly 60%. Those favoring independence grew from less than 10% to more than 20% over the same period. Overall, these two categories account for more than 80% of Taiwan’s population today. Those who favor reunification or reunification as soon as possible has dropped from just over 30% to less than 10%. At the same time, it is important to specifically point out that the proportion of people supporting the permanent maintenance of the status quo has risen from less than 10% to nearly 35%. This represents the most significant shift in Taiwanese perceptions regarding unification and independence.

The change in Taiwanese identity has been the most notable of all statistics presented. 30 years ago, the number of those self-identifying as Taiwanese was less than 20%, but in 2024, it is already more than 60%. At the same time, those who identified as both Chinese and Taiwanese dropped from around 50% to around 30%. It’s just the Chinese who have dropped from about 25% to negligible.

There is also a big change in party leanings, with neutral or independent identifying voters dropping from over 60% to around 30%, and the Kuomintang (KMT)’s approval rating fluctuating from around 35% down to a low of around 15% when Lee Teng-hui left office, to a high of close to 40% in Ma’s first term, and then a steady decline since then to around 20% today. The Democratic Progressive Party (DPP), on the other hand, has maintained an overall upward curve, from 3% to 30%. The Taiwan People’s Party (TPP) has seen its support rate rise from less than 1% to more than 15% in the five years since the party’s founding creating a true three way party split with a about 30% of voters switching between the parties. .

When analyzed comparatively, a few trends are notable: it is evident that the downward trend in the number of individuals who are neutral or unresponsive in terms of political party preference aligns closely with the declining trend of those who identify as both Chinese and Taiwanese.

Conversely, the upward trend in support for the Democratic Progressive Party (DPP) corresponds closely with the increase in individuals who identify solely as Taiwanese and those who favor Taiwan’s independence. Correlation suggests this is connected to the Democratic Progressive Party (DPP)’s stance on Taiwanese independence. However, despite this, we consistently see note the proportion of those pushing for immediate independence or leaning towards it remains far lower than those who prefer to maintain the status quo (26.2% vs. 60.9%). The Democratic Progressive Party (DPP) ‘s core support likely comes from the 26.2%, while the remaining supporters are less stable.

It seems the rise in Taiwanese identity and the significant growth in support for permanently maintaining the status quo reflects the sentiment among Taiwan’s younger generation, particularly those who have grown up after the relatively stable cross-strait relations that followed the 1996 Taiwan Strait Crisis. This generation is not inclined to go to war but also does not want independence. A survey released by Taiwan’s Global Views Monthly in January 2022 showed that if war were to break out between Taiwan and Mainland China, 51.3% of the public would be unwilling to go to the battlefield themselves or send their family members, with only 40.3% willing to do so. Among those aged 20 to 29, the percentage unwilling to fight is the highest, reaching 70.2%.

After analyzing all the data, it is again possible to identify certain specific points in time and events that influenced the decisions of the Taiwanese people. For example, the Hong Kong Protests in 2019 gave a steep rise in the Democratic Progressive Party (DPP)’s support; these voters are Taiwan independence supporters who solely identify as Taiwanese and responded to the failure of the administration.

Overall, the degree of division within Taiwanese society is significant, but it is not as absolute as in the United States. Compared to social issues, the divisions in Taiwan are more focused on government performance, external factors, and the highly sensitive cross-strait relations. This has resulted in Taiwanese politics being more susceptible to external influences. The question then arises: if war were to come, would Taiwanese people be able to defend their land like the Ukrainians? Would the cost to China of reunifying by force not be worth it for the Taiwanese. At least from the data, this seems challenging.

III. Cost Benefit in the Long Run with Invasion

1. Economics Factor

Contemplating an invasion of Taiwan is difficult, but it is possible to model the costs and benefits of such an invasion and understand what might happen. Looking first at the economic situation, China has recently weakened economically. Before 2019, China’s rapid economic growth was considered a global miracle. The Communist Party of China (CPC) saw this as, and in fact it became, its primary source of legitimacy—the massive success of China’s system under CPC leadership. However, since the outbreak of the COVID-19 pandemic in 2020, several economic and social governance issues have emerged. The traditional “three driving forces” of Chinese economic growth in China are now all in decline

First, due to China’s overcapacity and insufficient domestic consumption, exports became a key factor in maintaining economic growth in the 2000s. However, as major export destinations like the United States and the European Union applied political pressure and protectionist trade measures against China, exports ceased to be a significant driver of China’s economic growth. The decline in exports has led to difficulties for millions of Chinese blue-collar workers, and small factory owners have become some of the most economically vulnerable groups in Chinese society.

Second, a significant portion of economic growth, especially in real estate and infrastructure investment—once the backbone of the economy—was funded by massive loans from property developers and local government hidden debt financing (known as municipal investment bonds). While these actions previously generated vast wealth and development for China, they now must be paid back and because incomes have stalled paying these loans back is finically very difficult. Since 2021, with the debt defaults of landmark real estate companies like Evergrande, the era of real estate and infrastructure driving China’s economic growth has collapsed. Similarly, following the pandemic, foreign investment in China has decreased, with many investors withdrawing their funds from the country. Domestic investment institutions in China have also faced difficulties in raising funds, making it hard for startups to survive. Now, only companies aligned with China’s governmental development strategies, such as those in semiconductors and artificial intelligence, are likely to secure funding. The rapid decline in the number of newly established unicorn companies each year and the overall decrease in financing reflect that this second economic driver is no longer effective.

Third, with the impact of the first two factors and the general pessimism about future economic and political development, consumption levels in China have also been weak. While wealthy individuals continue to shop in high-end luxury stores, a majority of people believe saving their money in banks is the safest option. All three driving forces have stalled, and within five years, China’s economy has fallen from its steep upward trajectory and is now teetering.

2. Military Factor

Beyond the economic struggles, Taiwan indeed remains a challenging place for military action and here again, it seems difficult for China to succeed. First, the Taiwan Strait, which separates Taiwan from Mainland China by about 180 kilometers, has complicated maritime conditions, and the large number of commercial vessels make it difficult to create a fully controlled battlefield without advance notice, preventing the possibility of a surprise attack. Additionally, any amphibious landing in Taiwan must be conducted on its western coast, where landing points are very limited. The Taiwanese military can easily pre-position sea mines at ports and landing sites, significantly hindering the People’s Liberation Army’s (PLA) operations. Moreover, Taiwan’s rugged terrain would allow Taiwanese forces to replicate the guerrilla warfare tactics used by the Communist Party of China (CPC) during the Chinese Civil War and the Anti-Japanese War, potentially inflicting heavy casualties on the PLA.

At the same time, the potential involvement of third parties is also a crucial consideration. While the likelihood of direct intervention by the U.S. and Japan in a conflict is low, actions such as airlifting ammunition and weapons to Taiwan, maintaining a presence on the battlefield to help Taiwan monitor the movements of the People’s Liberation Army (PLA), and other similar support measures could result in accidental involvement in the war.

3. Sanctions

There can be no doubt about China’s ability to militarily defeat Taiwan. At the same time, I do not believe that the so-called “blockade strategy” would be effective. On the issue of sanctions, China’s strategy is to hold the West, particularly Europe, economically hostage, and this strategy has a far greater chance of success than Russia’s. However, from the perspective of how the West might respond, if we assume China is determined to recover Taiwan by force, the best way to mitigate the risks would be to continue a firm process of “decoupling” from China, spreading the inflationary impact over the coming years. Yet, this seems unrealistic. The U.S. is in an election year, and the Biden administration is eager to smooth its path to reelection, has focused on reducing inflation, which led to an increase in U.S. imports from China in the first half of this year. Thus, “decoupling” appears at odds with the current political realities. China, with its long-term one-party rule, does not face such pressures.

The effectiveness of potential American sanctions is worth considering. China’s annual trade volume with the EU and the U.S. exceeds $1.5 trillion. No matter how much countries push for “decoupling” from China, it’s impossible to eliminate such a large amount of trade within just a few years. Industrial relocation is a long process, not one that can be driven solely by political will. If the goal is to cut the volume of trade in half, and it is possible to reduce trade by 10% per year, it would take until 2031 to reach the goal, which is certainly later than the 2027 “deadline” many scholars predict as China’s potential last opportunity to take Taiwan by force. While I am not a supporter of this “last deadline,” if the downward trend in trade is already established, China might be more motivated to attack Taiwan. In other words, when China believes the value of recovering Taiwan outweighs the value of maintaining U.S.-China or China-EU relations, it will make the decision to take Taiwan by force, and vice versa.

Of course, the above perspective is influenced by other factors. First, I don’t believe that the West and China can reduce trade by 10% per year. Especially in the case of China-EU trade, China’s grand strategy is quite clear—maintain absolute alliances with countries like Russia, Iran, Syria, and North Korea; cultivate close relationships with valuable Belt and Road countries like Saudi Arabia, Vietnam, and Indonesia; and adopt a strategy of friendliness toward Europe while responding firmly to specific actions (mainly countering certain European policies). Europe holds more leverage than China, and it is still under the influence of the United States, as evidenced by Ursula von der Leyen acting as a U.S. proxy, showing the U.S. continues to wield significant influence in Europe.

This suggests significant rifts could emerge among Western nations when it comes to sanctioning China. Some countries might opt for relatively symbolic actions, like freezing Chinese officials’ assets in their territories. On trade, recent developments show Germany and Spain putting pressure on the EU to reconsider imposing additional tariffs on Chinese electric vehicles, illustrating the deeply divided views on China within the EU. In specific cases, national interests lead to differing opinions— for instance, Germany opposes tariffs on Chinese electric vehicles to protect its luxury car sales in China, while France, with a weaker automobile industry, supports the tariffs.

In this way, the economic entanglement between the West and China differs greatly from that with Russia. The West’s economic ties with Russia were insufficient to prevent them from imposing significant sanctions on Russia in exchange for safeguarding their interests. Russia initially sought to leverage its oil and natural gas to hold Europe hostage. This strategy unsettled Europe at first, but as the U.S. and Middle Eastern countries increased their oil and gas supplies to Europe, the crisis was averted. However, the interdependence between China and Europe is different. The ties between Russia and Europe could be severed because substitutes were available. But there is currently no alternative to China’s industrial production capacity. If the price of toilet paper in Europe rises to €10 per roll, such sanctions would inevitably backfire.

In summary, if China and Taiwan go to war, the severity of American and European sanctions will depend on China’s performance on the battlefield and whether the West can firmly commit to decoupling from China over the next few years. Otherwise, the sanctions could backfire on themselves.

4. International Factors

Whether China can gain sufficient support or neutrality from the international community is a key factor in the war. China has consistently emphasized the Taiwan issue is a matter of its domestic affairs, distinguishing it from the Russia-Ukraine conflict or the Israel-Palestine issue. However, several Western countries have recently pushed for a reinterpretation of the United Nations General Assembly Resolution 2758. The U.S. Congress passed a bill stating that “Resolution 2758 only addresses the issue of China’s representation, and does not involve Taiwan.” Similarly, an Australian parliamentary bill claims that UN Resolution 2758 “does not establish the People’s Republic of China’s sovereignty over Taiwan, nor does it determine Taiwan’s future status in the United Nations.”

If a resolution similar to the one condemning Russia’s “illegal annexation” in Ukraine were to be presented at the UN regarding Taiwan, how might the vote play out? First, China would likely receive more votes in favor than Russia or Israel, but the “In Favour” votes would still be an absolute minority. Some Southeast Asian, Central Asian, and African countries might vote in favor, along with perhaps Russia (even though China abstained on the vote regarding Russia’s actions). However, many countries would likely abstain, and the number of abstentions would be much higher than those seen in votes involving Russia and Israel. Ultimately, Western nations, along with Japan, South Korea, India, Australia, and New Zealand, would likely lead the opposition. To gauge whether China has sufficient international support, we would need to look at the combined total of “In Favour” and “Abstention” votes and see if this surpasses the “Against” votes. Given the expected large number of abstentions, such a resolution would likely pass, resulting in a formal condemnation of China and possibly even demands for China to withdraw from Taiwan.

However, since this type of resolution carries no legal weight, China would likely not be overly concerned. International bodies like the United Nations would probably adopt a neutral stance, refraining from taking a strong position. The most important factor in this situation is the attitude of Southeast Asian and Middle Eastern countries. Take Vietnam as an example—it could potentially vote “In Favour,” “Abstain,” or “Against.” If countries like Vietnam vote against China, it would indicate that they have lost confidence in China’s future and do not believe that China can outcompete the West in the long term. A vote of abstention would suggest these nations anticipate China facing greater international pressure, but it can still maintain a regional presence. A vote “In Favour” would demonstrate that Vietnam’s leadership believes China will eventually replace the U.S. as the dominant power. The stance of other Belt and Road Initiative countries would similarly reflect their optimism toward China, and by extension, their expectations for the outcome of a potential conflict.

Regardless of how the votes turn out, China’s international image would undoubtedly suffer. However, the actual impact on China’s global standing would be limited. These votes would serve more as a barometer for assessing how other nations perceive China’s future and its prospects in the global arena.

IV. Conclusion

The Taiwan Strait has maintained peace for nearly 80 years, but the Communist Party of China (CPC)’s desire to reunify Taiwan has never waned with time. Whether peaceful unification is possible and if this peace can continue beyond 80 years is a multi-dimensional issue, encompassing domestic, international, economic, livelihood, and public opinion factors. With the changing geopolitical landscape in recent years, the tension in the Taiwan Strait today is at an all-time high and continues to escalate. The future of Taiwan remains uncertain.

Data Collection Methodology

I. Data Sampling and Analysis

The research target population for each survey is the adult population 20 years or older in the Taiwan Area (i.e. excluding the offshore islands of Kinmen and Matsu). Sample for every landline phone survey is drawn from telephone books, with the most recent year’s set of China Telecom Residential Telephone Number Books serving as the population. Each sample is constructed from numbers listed in each county and city telephone book and is drawn proportionately from all residential phone numbers across the island. In order to ensure complete coverage, after systematic sampling produces a sample for each city and county, it is then supplemented as circumstances warrant based on the last one or two or three or four digits to include households with unlisted numbers. After phone contact is established, the interviewer follows the specified intra-household sampling procedure to identify the targeted member of the household, and begins the interview.

The mobile phone survey is based on the “Mobile Communication Network Business User Number Allocation Status” published by the National Communications Commission (NCC) (the allocation status of the first five digits of the mobile phone number), combined with randomly generated codes/numbers representing the last five digits of mobile phone numbers to create the phone sample.

In order to ensure that the sample structure is more representative of the population, key sample variables are used to weight the sample’s partial characteristics through an iterated (raking) process. These include weights for sex, age, education and geographic location calculated from the Taiwan-Fukien Demographic Fact Book, Republic of China, published by the Ministry of the Interior.

The chart of trends in core political attitudes among Taiwanese is based on data gathered through this center’s telephone survey polls. Survey data is merged annually to generate data points, except those released in June; they come from surveys conducted between January and June. After results are weighted, the figures for the three main variables are parsed out and added to the trend chart. Percentages are rounded to the nearest tenth, so the total of the percentages may not be 100%.5

II. Time of Coverage and Sample Sizes

The data presented in the current trend chart includes that from 1992 through the first half of 2024. The interview sample sizes for each year are detailed below:

Year Cases

1992 4120

1994 1209

1995 21402

1996 10666

1997 3910

1998 14063

1999 9273

2000 11062

2001 10679

2002 10003

2003 14247

2004 34854

2005 7939

2006 13193

2007 13910

2008 16280

2009 20244

2010 13163

2011 23779

2012 18011

2013 13359

2014 20009

2015 22509

2016 15099

2017 13455

2018 9490

2019 16276

2020 11490

2021 12026

2022 12173

2023 14933

2024 61516

III. Main Variables

1. Taiwan Independence versus Unification with the Mainland (TI-UM) The independence-unification (TI-UM) position is constructed from the following survey item: “Thinking about Taiwan-mainland relations, there are several differing opinions: 1. unification as soon as possible; 2. independence as soon as possible; 3. maintain the status quo and move toward unification in the future; 4. maintain the status quo and move toward independence in the future; 5. maintain the status quo and decide in the future between independence or unification; 6. maintain the status quo indefinitely. Which do you prefer?” In addition to these six attitudes, the trend chart also includes non-responses for a total of seven categories.

2. Political Party Identification (PID)

The political party identification variable was constructed through the following steps. The respondent is first asked the following: “Among all political parties in our country, which party do you think of yourself as leaning toward?” If the respondent does not answer unequivocally, then s/he is asked “Relatively speaking, do you lean toward any political party?”. If the respondent then names a party, that answer is taken to be his/her party ID; and if the respondent still does not indicate a preference, the answer is counted as a non-response and others.

3. Taiwanese Identity

The following survey item was used in all instances to construct the measure of Taiwanese identity: “In our society, there are some people who call themselves ‘Taiwanese’, some who call themselves ‘Chinese’, and some who call themselves both. Do you consider yourself to be ‘Taiwanese’, ‘Chinese’, or both?”. Responses are scored into one of fourcategories: Taiwanese, Chinese, both, or no response.

Statement of Copyright

The contents displayed on this website, including but not limited to text, figures, formatting, audio and visual recordings, and other information, are without exception protected by copyright. Under copyright law, materials on this website can be downloaded for private use, under the condition that the user includes the following acknowledgement: “Source: Core Political Attitudes Trend Chart, Election Study Center, National Cheng Chi University.” This webpage may not be reproduced in part or in full without permission of Election Study Center. Without express approval, this website or any part of its contents may not be reproduced, broadcast, presented, performed, transmitted, modified, disseminated or used in any other activity covered under the standards of current copyright law. Actions taken in proper accordance with copyright law are not subject to this restriction.

Works Cited

Chao, Linda, and Ramon H. Myers. The First Chinese Democracy: Political Life in the Republic of China on Taiwan. John Hopkins University Press, 2003.

Copper, John F. Taiwan: Nation-State or Province? 5th ed., Westview Press, 2008. Dittmer, Lowell, ed. Taiwan and China: Fitful Embrace. University of California Press, 2017.

Hickey, Dennis V. Foreign Policy Making in Taiwan: From Principle to Pragmatism. Routledge, 2006.

Shambaugh, David. China and the World. Oxford University Press, 2020.

Tucker, Nancy Bernkopf. Dangerous Strait: The U.S.-Taiwan-China Crisis. Columbia University Press, 2005.

Nathan, Andrew J., and Andrew Scobell. China’s Search for Security. Columbia University Press, 2012.

“Taiwan Relations Act.” Public Law 96-8, 96th Congress of the United States of America, 1979. U.S. Government Publishing Office, www.govinfo.gov.

United States Department of Defense. Annual Report to Congress: Military and Security Developments Involving the People’s Republic of China 2022. Office of the Secretary of Defense, 2022, www.defense.gov.

Chen, Dean P. U.S.-China Rivalry and Taiwan’s Mainland Policy: Security, Nationalism, and the 1992 Consensus. Springer, 2017.

United Nations. Resolution 2758: Restoration of the Lawful Rights of the People’s Republic of China in the United Nations. General Assembly, 1971. United Nations Digital Library, digitallibrary.un.org.

About the author

Bill Wu

Bill Wu is a high school student in the U.S. with a keen interest in political science, focusing on American and Chinese politics. He is active in leadership roles, including as Secretary-General of his school’s Model UN, and is the founder of an AI education startup named XStudy AI. His work aims to bridge cultural gaps and contribute to global political discourse.

The post Why has there been no war in the Taiwan Strait? A Review of Chinese-Taiwan Relations 1948-Present appeared first on Exploratio Journal.

Introduction

For decades, Latin American political constituents have debated the most effective economic model to follow to ensure sustained economic growth whilst also reducing poverty and inequality. Argentina has experienced alternating cycles of socialism, clientelist economic models promoted by Juan Perón from 1955 until 1973 and then the Kirchner’s from 2003 until 2023, interlaced with brief periods of free-market economic policies supported by institutions such as the IMF, the World Bank, and the economic consensus promoted by prominent faculty members at the University of Chicago. The neo-liberal agenda was pursued sporadically by the dictatorship under Juan Videla followed by the administrations of Carlos Menem and Mauricio Macri.  All three attempts struggled to adhere to the free-market principles prescribed by the Chicago School and have subsequently failed to deliver improved standards of living across society. President Javier Milei, who was elected to office in 2023 on the promise of vastly reforming Argentina’s economy and political structure represents possibly the first time that a Latin American country adopts a neo-liberal economic agenda in its purest form outside the context of a military dictatorship. Milei’s presidency is truly an experiment to see if unfettered free-market policies are able to deliver positive outcomes and whether democratically elected political institutions are strong enough to withstand the societal pressures caused by severe economic hardship until reforms can bear fruit. 

Compared to other libertarian-leaning leaders, Milei stands apart due to the distinctive and radical nature of his neo-liberal approach to public policy. The closest comparison we can draw is with General Augusto Pinochet, who was at the front of Chile’s dictatorship from 1973 to 1990. Both leaders draw from the same libertarian economic philosophy as described by the Chicago School, which advocated for minimal public spending, lower taxes, flexible labor markets, deregulation, strategic privatization, free trade, and floating exchange rates, among others. More importantly, both regimes embraced a radical approach to reforms with the same degree of intensity (Centenera 2024). The key difference between these two experiments is that Chile’s military dictatorship imposed its agenda by curtailing individual liberties, while Milei’s presidency operates in an open democracy. Brazil under Jair Bolsenaro also implemented free-market policies but unlike Milei, his ideological focus was centered on social and cultural issues rather than economic ones. 

This research paper will focus on the extent to which Javier Milei has been able to successfully advance his neo-liberal reforms through Argentina’s political system, specifically the factors assisting and impeding his agenda. For context, it is important to look at examples historically of other attempts to implement a free-market model in Latin America generally and in Argentina specifically to ultimately gauge whether Milei’s economic vision has the potential to take root where past attempts have failed.

Literature Review

Latin America’s Early Liberalism 

In the 19th century, liberal elites, upper-class influential individuals, introduced Latin America to constitutionalism and modern representative governments. Latin American political systems evolved as elected presidents with broad powers replaced monarchs (Negretto and Aguilar-Rivera 2000). However, the road to successful constitutional democracies was a lengthy one as most liberal regimes in the region were unable to achieve co-existence with opposition parties and the necessary expansion of political representation (Negretto and Aguilar-Rivera 2000). The conventional view of the failure of liberal democracies in 19th century Latin America was that elected elites were unable to break with the mentality and the practices inherited from the authoritarianism of the colonial period and that institutions were unable to generate political consensus. Other scholars argue that frail democracies are the product of territorial fragmentation and factional conflict that led to the creation of a centralized government and tight control by the ruling elites. (Negretto and Aguilar-Rivera 2000). Because liberal elites manipulated elections through clientelism or fraud most liberal regimes in the 19th century evolved as oligarchic regimes with extreme inequality between the ruled and the rulers in terms of wealth, social position, and even race (Negretto and Aguilar-Rivera 2000). 

In the subsequent period, notably around the 1840’s, Latin American nations adopted foreign constitutional models to fit their local needs by adopting doctrines of federalism, or the division of powers (Negretto and Aguilar-Rivera 2000). In Argentina, specifically, the enactment of the constitution of 1853 was at the center of several political disputes. One of which was between Buenos Aires, which opposed the federal form of government, and the rest of the provinces, which believed it was the only viable means of organizing the national state (Negretto and Aguilar-Rivera 2000). Only in 1853, after the military defeat of Buenos Aires by the governor of Entre Rios, were the provincial governors able to enact a federal constitution. The Constitution was somewhat successful as it created formal institutions that helped solve the problem of national integration and consolidation of state authority. It also created a system of agreements among the elites regarding the division of power which helped gradually create more stability (Negretto and Aguilar-Rivera 2000). Despite these advances, the elites maintained control over the electoral process, which is why Argentina became an illiberal democracy at best (Zakaria 1997).  Not only was the electoral process tightly controlled, but statist and clientelist economic models were important levers to retain political power.

The Evolution of The Union Civica Radical and Peronism in Argentina

As constitutionalism across Latin America continued to unfold, it wasn’t until 1890, amid an economic crisis, that an organized opposition against the oligarchic regime in Argentina emerged in response to the political illegitimacy of the status quo. This overthrow gave rise to The Union Civica Radical, which became a dominant political force advocating for democratic reforms and broader electoral participation (Negretto and Aguilar-Rivera 2000). This political activism paved the way for a new electoral law that was enacted in 1912 during the presidency of Roque Sáenz Peña. The law established secret, and mandatory male suffrage along with a system of distributing seats in Congress that provided two-thirds of the total seats to the majority party and one-third of the total seats to the second most-voted party (Spruk 2019). After the law was passed, Argentina saw a significant increase in voter registration and voter turnout (Sabato 2001). It wasn’t until 1916 that the leader of the Union Civica Radical, Hipólto Yrigoyen, won the first presidency under The Sáenz Peña Law, which truly marked the end of an oligarchic regime and the beginning of a more inclusive and representative democracy (Spruk 2019). Ideas such as separation of powers, rule of law, and respect for individual rights fostered the political legitimacy that gradually replaced the authoritarian and colonial model (Negretto and Aguilar-Rivera 2000). 

However, these advances were short-lived, when in 1930, a military coup ousted President Yrigoyen which marked the beginning of the Infamous Decade which was characterized by electoral fraud, state interventions, and economic instability all of which were exacerbated by the Great Depression (Spruk 2019). In response, General Juan Perón was elected to power in 1946. Many scholars would argue that Péronism was the birth of populism, where political leaders promised immediate improvements in living standards in exchange for political support, even if the long-term effects were understood to be sub-optimal. This approach was disguised as an alternative to both capitalism and socialism. As a result, the Peronist movement became extremely influential, mostly attracting support from the working class, military sectors, and the Catholic Church (Spruk 2019). The movement was centered around what Juan Perón referred to as “justicialismo”, which emphasized social justice as a prime mover of national unity (Ciria 1974). 

In the economic field of his presidency, the state played a dominant role in order to quickly deliver benefits to constituents. Perón supported light and intermediary industries while large enterprises that could exercise political power were nationalized. The economy became inward-looking due to import-substitution industrialization policies, including raising tariffs and taxing exports (Huddle 1997). Perón also reduced foreign dependency and increased the production of goods domestically by providing local industries with subsidies and intervening to manage said industries (Huddle 1997). As a result of these economic policies, trade deteriorated making the import of capital goods, industrial raw materials, and fuel more expensive (Ciria 1974). This led to foreign reserves dropping and large public deficits prompting the government to increase paper currency printing and borrowing funds. As the monetary base increased, so did inflation (Huddle 1997).   

Peron faced the economic crisis with increasingly authoritarian and oppressive measures, which contributed to his downfall. By 1955, during Juan Perón’s second term as president, opposition had already begun to take shape, with discontent arising among sectors within the military, the middle classes, and the students (Ciria 1974). This growing dissatisfaction culminated in Perón’s ousting by a military coup later that year. Although Perón returned to power in 1973, the economic model he represented was again unable to deliver economic prosperity or stability.  Following Juan Perón’s death during his third term as president, his wife, Isabel Peron took over as president. Her presidency was short-lived as she was overthrown in 1976 by General Jorge Videla, who was committed to undoing Peron’s legacy. Although Peronism has undergone significant changes over the past few decades, it has maintained its position of being one of the largest political parties in Argentina, along with Union Civica Radical. 

Argentina’s Dictatorship: Introduction to Neoliberal-leaning Economic Policies 

The period under Jorge Videla, referred to as the ‘Dirty War,’ was characterized by widespread human rights abuses and the abduction of citizens by authorities with an estimated 10,000 to 30,000 people killed during this time (Hayner). Due to inflation that accelerated under Juan Perón’s last term, Videla was faced with a collapsing economy marked by soaring inflation. In response, Videla largely left economic policies in the hands of Minister Alfredo Martinez de Hoz, who attempted to restore economic growth by reversing Peronism in favor of a free market economy (Hayner). Whilst Videla may have embraced a few neo-liberal economic principles, such as liberalizing trade, he also borrowed heavily from foreign banks in order to fund rising public deficits due to a failure to curb public spending, a pronounced departure from the Chicago School recipe.  

Following the dictatorship and until the 2000s, Argentina experienced a gradual shift to neoliberalism as a response to the pressing debt crisis and its dependency on foreign borrowing.  This transition in Argentina mainly played out under Alfonsin’s and Menem’s presidencies.  Other Latin American countries began to embrace neo-liberal policies in response to the debt crisis that reached its peak in the 80’s (Huddle 1997). Countries started deregulating, privatizing, and opening themselves up to trade competition. Between 1985 and 1992 more than 2,000 publicly owned firms including airlines, public utilities, banks, and insurance companies, were privatized throughout the region (Huddle 1997). However, as neo-liberal policies grew in frequency and intensity, poverty and inequality grew across the region: 80% of new jobs after 1990 were created in the informal sector of the self-employed (Huddle 1997). 

The challenge faced by most countries is that it takes time for neo-liberal reforms to generate sustained growth, low and stable inflation, and permanent increases in wages. Sebastian Edwards, the World Bank Chief Economist for Latin America, stresses that free-market reforms produce greater macroeconomic stability and open up new avenues for development and growth. Nonetheless, Edwards acknowledges that the transition can be difficult and painful and may require short-term alleviation of dislocation in order to ease the impact. On the other hand, Duncan Green, author of Silent Revolution, argued that neo-liberal reforms in Latin America were excessively costly, as countries were forced into longer recessions than necessary in order to generate the trade surpluses required to meet debt and interest payments (Huddle 1997). Additionally, he argues that the bad far outweighs the good as restrictions in public sector programs hurt the poor and worsened income distribution across Latin American countries. These discrepancies between Edwards and Green reflect a much larger debate in society on whether the short-term losses and damages inflicted by neo-liberal policies are justified by the long-term benefits in the economy. This is the debate raging in Argentina today.

Backlash and shift to the left 

In the 2000s, this neo-liberal movement faced backlash regionally but more so in Argentina, where a balance of payments crisis resulted in the collapse of the currency peg in January 2002, which had supported the value of the peso since 1991 as part of an effort to combat hyperinflation and stabilize the economy (Spiegel). As a result, Argentine household savings were decimated with depositors unable to withdraw their savings as the Central Bank struggled to contain further capital flight. This economic crisis led to political chaos and eventually to a resurgence of a brand of Peronism characterized by extreme government intervention in all aspects of the economy. Initially championed by Néstor Kirchner, his wife and successor, Cristina Kirchner, took interventionist policies to new levels during her term.

After several years of falling living standards capped by a public corruption scandal engulfing Cristina Kirchner´s administration, Mauricio Macri, the former mayor of Buenos Aires, was elected president and served between 2015 to 2019. Macri, a liberal right-wing conservative, believed that the gradual implementation of economic reforms was needed in order to maintain support and avoid the most painful consequences of fiscal austerity. However, a gradualist approach was unable to deliver balanced budgets, which were critical to generating investor confidence and curtailing capital flight. Consequently, despite its initial success, political support for Macri’s reforms wavered once numerous economic risks began to materialize (Kovalski 2019).

Although Milei’s neo-liberal reforms may resemble those that Macri attempted to introduce, Macri barely got across the starting line, while Milei is already in a full sprint on the path to a pure neo-liberal economic model. 

Methodology

To assess whether Milei has successfully advanced his neo-liberal reforms through Argentina’s political system, I conducted primary research by interviewing key political figures and opinion leaders in Buenos Aires during the summer of 2024. These included heads of government secretariats, union leaders, NGO directors, and Argentine professors specializing in political science. In total,  I conducted eight interviews which allowed me to gain a diverse range of voices and perspectives. Before conducting the interviews, I researched in depth the interviewees and prepared around ten questions for each of them. For each of the interviews I also voice recorded the conversation and later translated the recording into English. These transcripts were useful in the terms of notetaking and synthesizing my research but also for cumulating quotes from the interviewees. This variety of perspectives has been largely shaped by the country’s long history of polarization which has formed the complex political landscape in present-day Argentina. These diverse perspectives help explain why many of Argentina’s institutions have supported and facilitated the implementation of Milei’s aggressive plan despite historical opposition to free-market policies, while others have clearly resisted a departure from Peronism. These diverse views mirror the debate between Edwards and Green on whether the short-term losses and pain inflicted by neo-liberal policies are justified by the long-term benefits in the economy. In this case, however, the stakes are particularly high, as the future of Argentina’s economy is on the line. The economic policies implemented in the coming years will determine whether Argentina’s economy will continue to decline or if it will begin its path to recovery. 

Who is Javier Milei?

A combination of the interchanging cycles of Peronism and neo-liberal leaning economic policies, Videla’s dictatorship, and the discrediting of Peronism as interpreted by the Kirshner’s all contributed to the poor and vulnerable conditions in which the 2023 general presidential elections took place. A decade of poor economic management resulted in inflation that was over 135%, a poverty rate above 40%, and a GDP growth of -2.5% (European Parliament). In this environment, against conventional wisdom, President Milei, also given the name El Loco– The Madman- won the 2023 elections. Milei, a trained economist with a degree in economics from the University of Belgrano has navigated diverse careers, from being the lead singer of a Rolling Stones cover band called Everest, to becoming a well-known economic pundit on Argentine TV (Phillips). And just five years ago, he embarked on his political journey, being elected as a national deputy in 2021 under his party, La Libertad Avanza. 

During Milei’s campaign, “he brandished a chainsaw at rallies to symbolize his plans to slash government spending, dressed up as a superhero who sang about fiscal policy and told voters that his five cloned English mastiffs, which he reportedly consults in telepathic conversations, are his ‘best strategists’” (Bergengruen and Werning). The self-described ‘anarcho-capitalist’ pledged that if he won office he would eliminate the nation’s central bank, dollarize the economy, and dismantle the corruption-riddled state controlled by parasitic career politicians  (Bergengruen and Werning)(Kozuel-Wright). Milei in an interview with the Times writer Vera Bergengruen said, “‘Argentina will become a model for how to transform a country into a prosperous nation… I have no doubt’” (Bergengruen and Werning). 

Milei won the second round of the general election with 55.69% of the votes, surprising many of his critics. According to analysts, Milei’s votes primarily came from the middle and lower classes, especially young people (European Parliament). Milei’s political rhetoric since his campaign has “aimed to address the simmering anger and frustration within a society worn down by persistent economic crises and instability” (Belgrano). This message strongly resonated with many vulnerable groups, who felt angered by the difficult circumstances created by the last four years under the administration of Alberto Fernandez, a Kirchner acolyte. Milei has addressed the public by using a populist-style discourse, particularly employing an anti-establishment message by blaming the “political caste,” referring to the elites, for Argentina’s economic, social, and political crisis (Buenos Aires Times). Through Milei’s populist rhetoric and limited political experience, he has portrayed himself as a leader who serves as a representative of the people. Milei has conveyed a message of empowerment and freedom to young people, sparking a profound cultural shift. He has inspired younger generations to embrace their potential and achieve prosperity. This message and cultural transformation have resonated strongly due to Argentina’s desperate situation. In a conversation with the Secretary of Small and Medium Businesses, Marcos Ayerra remarked, “we were so close to being like Venezuela.” Argentina has been so weakened by poverty, and people have reached their breaking point. Macri didn’t have the cultural and social determination that the people have now. Now, however, people have reached their threshold, and it has driven them to take the lead in shaping a new cultural direction, and Milei is at the face of this change.  

Sources of support and facilitation of reforms  

Milei’s Unique Approach and Early Economic Success 

Since Milei has been in power, he has implemented significant reductions in public spending, which has greatly contributed to overcoming a period of hyperinflation (Stuttaford).  Monthly inflation has come down from 26% in December 2023 to around 4% in June, where it has remained (Murillo). Additionally, in January, for the first time since 2012, Argentina registered a primary fiscal surplus (Centera 2024). Milei transformed a fiscal deficit of 2.7 percent of GDP into a surplus of 1.2 percent of GDP by freezing pensions and public salaries (Murillo). The Undersecretary of Innovation, Santiago Pordalenne, explains how the public has supported Milei so far as he has shown them “that there is light at the end of the tunnel.” Although Milei publicly shares how he is not a politician and how he will do everything politically incorrect, he tells the public he will do everything in his power to improve their lives, provide better job opportunities, and alleviate them from their misery. This message resonated strongly among the public and especially came through to the younger generations. Pordalenne explains how Milei is convinced that the transformation of Argentina must be abrupt and not gradual, unlike President Macri who considered that the change should be gradual.

Adaption and Reliance on Private Sector

The Secretaries of Entrepreneurship, Small and Medium Businesses, and Innovation have contributed to advancing Milei’s core policies and his neo-liberal vision. They have done this by working on  facilitating conditions and adapting and relying on the private sector to help stabilize key macroeconomic variables, which they believe will lead to microeconomic stability. This is relevant as it has been particularly challenging to create economic conditions that promote business creation and capital formation in Argentina. 

Ayerra, the Secretary of Small and Medium Businesses identifies Argentina’s three primary challenges as a) the macroeconomic problem, b) the labor issue, and c) the microeconomic problem. Ayerra explains that for decades, the Argentinian government practically gave everything away for free, funding subsidies, public services, and low-cost utilities by running sharp public sector deficits funded by printing money. This approach inevitably led to unrealistic expectations and a bloated fiscal budget.

Focus on Human Capital and Training

Milei’s government, in order to reduce public spending, has eliminated most subsidies for public services, the prices of which have spiked. He has also reduced government jobs by as much as 43%. With these job losses, the Secretariat has been working to facilitate the workers’ transition to the private sector. To do this, Pordalenne explains they have been training and forming the population, specifically the vulnerable classes.

Despite record-high unemployment, Argentina has various sectors with numerous job openings that remain unfilled due to challenges recruiting qualified employees. The Secretariat of Small and Medium Businesses has focused on training younger workers, because as Pablo Gutierrez, the Undersecretary of Entrepreneurship, explains, the broader Argentine public educational system from schools to secondary education, as well as post-graduate programs, does not prepare either entrepreneurs or future employees. Gutierrez explains that being an entrepreneur often involves trial and error, making mistakes along the process, and learning from them. He says that this resilience results in high-quality entrepreneurs because they are able to manage obstacles. However, there are a few risk-taking entrepreneurs in Argentina, a situation often attributed to years of socialist and protectionist governments that provided large segments of the population with guaranteed and protected jobs for life.

 Through this training, not only does the SBA hope that it will help transition workers to the private sector, but that it will also reduce informal employment. Ayerra states that “45% of employment is informal, and the SME, which make up 85% of the companies, account for 70% of informal employment.” Ayerra attributes the high informal employment to previous socialist-leaning Argentine governments, which imposed heavy tax and regulatory burdens pushing people to operate informally to avoid these costs. Informal employment comes with inadequate social protection and limits mobility. Additionally, being in the informal sector, makes it difficult for employees to access financial credit as they lack a formal credit history. Gutierrez explains how his Secretariat has been working on training programs, offering online certifications in financial education, and has been collaborating with bank associations to centralize all financial information for businesses interested in credit options, loans, and financing programs.  

Argentina in the Global Economy

Both the Secretariat of Innovation and the Secretariat of Entrepreneurship have also been working on repositioning Argentina in the global economy. Pordalenne states that the previous administration under Alberto Fernández was isolated from the global stage due to close ties with countries that included Venezuela, Nicaragua, and Russia. These relationships coupled with several sovereign debt defaults hindered Argentina’s ability to fully integrate into the global economy. 

However, Gutierrez explains that a shift towards a more open and competitive economy comes with challenges. Many firms, that have been accustomed to closed protectionist policies, are finding it difficult to compete globally. Many of them complain that they are unable to compete if their taxes aren’t reduced. For this reason, the government has been working on lowering inflation, taxes, and national consumption to address this concern. These tax reductions combined with lower inflation and a reduction in import tariffs will increase efficiency and productivity.

Milei’s government has also been focused on attracting more foreign investment and promoting the export of Argentine technologies and talent. However, it will take some time for multinational companies to start investing in Argentina as they need to see that changes are permanent, and only then will direct investment materialize. Argentina needs to rebuild trust and reinsert itself into the world as a ‘new’ Argentina, reliable, predictable, and with clear rules. 

Challenges and Opposition Facing Milei

Evolving Mindsets: Breaking Free from Socialist and Regulatory Constraints

The private sector’s role in emphasizing human capital and training, as well as repositioning Argentina in the economy, is a challenging undertaking. This is mainly due to the fact that, historically, Argentina has had governments that heavily regulated the economy, imposing rules that the private sector had to follow. This government aims to do the complete opposite, Pordalenne explains. They’ve been working on taking the pressure off the private sector and leaving it to do what it knows how to do, which is invest, generate employment, develop, and export. With its historical record, Argentina faces not only the challenge of addressing the lack of skills of employees but also struggles to change the mindset of the public that has been ingrained by past socialist, regulatory governments. Due to subsidies and free jobs being handed out, the Argentine people have been living a culture of not viewing work, employment, and hard work as a merit, as they’ve been historically oriented with the idea that putting effort and hard work is useless as the state can do everything for you.  

Gutierrez explains that Argentina’s history of having a lot of bureaucratic red tape was often an excuse for a process to be handled by intermediaries connected to corrupt networks who demand guarantees or bribes to approve a process. Despite these challenges, the government has attempted to free the public sector from the weight of the government and give more businesses more freedom and responsibility, by eliminating these processes that reduce corruption, inefficiency, and bureaucracy.  At the same time, Pordalenne states that the government is at the private sector’s disposal, listening to their needs and making the road they have to travel as simple and with as few obstacles as possible. They have been working on easing the process for businesses in the private sector and creating an environment where more opportunities are accessible. 

Labor Unions and Growing Inequality 

Milei has received significant opposition as his neo-liberal reforms support Duncan Green’s argument that such brutal policies exacerbate inequality and harm the middle class. Although there seems to have been progress these last couple of months, the General Secretary of the General Union of the city of Buenos Aires, Hector Daer, explains how in reality, this adjustment has been brutal, especially on the sectors that are bearing the brunt. Daer states, “there are sectors that can’t take it anymore.” Economist Juan Manuel Telechea, would agree with Daer, as he has said that this positive data has created “a false sense of success,” he also questions “‘how much more are we going to be able to cut social programs and pensions?’- and hide the fact that a great social crisis is brewing beneath the surface”(Centenera 2024).

 Milei’s first cut was shrinking the number of ministries from 19 to nine. He also ordered the freezing of almost all public works and devalued the peso by 54%. This adjustment has included a 64% reduction of the multi-billion dollar subsidies which is how the previous center-left government kept public transportation and various utilities- such as electricity, gas, and water at relatively low prices (Centenera 2024). Economist Juan Manuel Telechea says, “Milei is governing the free market and turning his back on society.” With a significant cut in public spending and causing a deep recession, this year among 46 million inhabitants there are almost 5 million who suffer from hunger. As a result, attendance at free community kitchens reached levels similar to that during Covid 19 (Centenera 2024).

As a result of this growing poverty and inequality, Milei has received significant opposition from leftist political parties, labor unions, and social organizations because of his brutal “shock therapy”. On December 20th, two weeks after Milei went into office, Milei issued an emergency decree to alter 366 laws with the aim of privatizing the country. The edict also, took away several worker’s rights, by reducing maternity leave pay and severance pay (Kozul-Wright). The decree immediately sparked protests among workers, following an appeal from the CGT. 

A few days later Milei sent a reformation bill, known as the Omnibus Law to Congress- after facing obstacles from both Labor Unions and Congress that impeded his ability to fully enforce his decree. This law called for significant deregulation approaches, the privatization of major state enterprises, and reform of the public education system, etc (Alexandra). In addition to these major spending cuts, the bill sought to undermine the fundamental principles of open democracy by proposing to scrap proportional representation in Congress. It also sought to transfer legislative power to the president in areas such as energy and fiscal policy until 2025 (Kozul- Wright). In opposition to what the public viewed as “power-grabbing measures,” just 45 days after Milei took office, Argentinian workers coordinated by The General Confederation of Labor (CGT) went on a general strike (Kozul- Wright). 

Despite Milei’s challenges in advancing his legislative agenda, with both his Omnibus law and Emergency Decree initially being rejected, he eventually succeeded in getting a significantly scaled-down version of the Omnibus laws passed. This revised version was approved by Congress on June 12th, 2024 and is in force today. 

Daer finds it particularly unjust that Milei’s administration has attacked labor rights purely to transfer wealth from workers to business sectors. When this happens he explains, “we band together, raise worker’s union consciousness, draw strength from the power workers give us and resist to prevent further encroachment on these rights.” Daer also criticizes how the state has just been sitting back, as it believes once the macroeconomy is in order the microeconomy will develop in itself, but hasn’t given the public any direction or support. Daer urges that the state needs to work on creating development conditions that generate social sustainability. 

Argentina’s Political System 

Since Milei has been in power, Argentina’s political system has also appeared to be an obstacle for Milei and his ability to implement his radical neo-liberal agenda. Milei’s party, La Libertad Avanza, which was created by him and his siblings back in 2021 to allow him to run for the presidency, only controls seven out of 72 senators, 38 deputies out of 257, and does not control a single provincial governorship or local mayor’s office (Bergengruen and Werning)(Binetti). Milei’s lack of representation in the government, coupled with the strong presence of Peronist representation in both chambers of Congress deputies, and senators, has proven to be extremely challenging for him to implement his radical structural reform, as any laws he proposes must be approved by Congress. 

This became clear when, following a strike by the General Confederation Labor and the rejection of key measures by Congress, Milei withdrew the Omnibus bill on February 6th.  Milei, however, told the Financial Times, that rather than seeing the bill “shredded,” he planned to wait until after midterm legislative elections in 2025 to try again with a comprehensive package (Nugent and Stott). In speaking with Manuel Solanet, the director of Public Policy at ‘Libertad y Progreso,’ an organization dedicated to public policy research and advocacy for economic freedom and market-oriented policies, he expressed his hope that in next year’s election, Libertad Avanza will secure a larger role in government while Peronism will lose influence. This shift, he believes, would facilitate more approval of legislation proposed by Milei. 

Milei, however, despite the hurdles he has encountered with Argentina’s political system, has pushed to find other ways to implement his radical neo-liberal agenda, including by issuing emergency decrees. Emergency decrees are enacted in response to emergency and exceptional circumstances, as the President bypasses Congress to pass the mandate. A couple of weeks later, and following the several protests that arose, during the CGT appeal, the court deemed Milei’s reform “unconstitutional,” and in March, Argentina’s senate voted to reject the decree. Manuel Solanet explains how emergency decrees have their limits, as although they are designed to allow presidents to respond quickly to emergencies, they also require congressional approval. 

Effects of Subsidy Removal 

Alejandro and Miguel, the executive director and the president of an Argentinian NGO, expand on this perspective. Their NGO, Vivienda Digna focuses on improving housing conditions for low-income families through programs like home improvement microloans, urban development projects, and social construction materials stores. However, upon speaking to them they shared how this reform adjustment has impacted them significantly. Miguel explains how before Milei’s administration the government was a source of finance for Vivienda Digna. Vivienda Digna still has an agreement they signed last year where they were assigned funding to build 50 homes as part of this project that they are just now starting, but the government has completely cut all contributions. 

In the past, Miguel explains, all the state projects related to assisting the people who needed it the most had not been working. The state was very incapable of solving this issue, they should have been because they had the capability of having a transformative impact but they weren’t. Therefore, with these difficulties, when the state finds an organization like Vivienda Digna, which can manage things in an orderly way and achieve good results, it’s also their interest to work with organizations like them to solve problems they don’t know how to address. However, with the inflation that Argentina was having and is still recovering from, the government has made it very clear to all organizations that there is no money left to contribute. 

Conclusion and looking forward 

Through evaluating sources of resistance towards Milei’s ‘shock therapy,’ it is evident that it has been extremely painful for not just the lower class, as it has deepened inequality and increased poverty, it has also been harmful- although to a smaller degree- to businesses that are attempting to be more competitive and accessible to others. However, despite this pain inflicted on all of the Argentine population most of the population is in consensus that these are the necessary measures needed to be taken to recover Argentina from its economic crisis. Pordalenne often uses the metaphor of a patient who- if he doesn’t do something is going to die. Therefore in order for him to survive he has to undergo surgery and rehabilitation. The rehabilitation is going to hurt, but Pordalenne explains that the necessary surgery is being done to revive Argentina. 

Through this research, I have found that Milei, so far, has managed to sustain popular support, even amid brutal economic adjustments and domestic recession, largely due to his success in reducing inflation and achieving a fiscal surplus (Stuttaford) (Murillo). However, social unrest is starting to fuel, as Milei has yet to address the growing inequality and poverty in the country. 

In order to sustain popular support and advance his transformative agenda, Milei will soon need to address the two primary barriers hindering the full implementation of his neo-liberal policies. These challenges include the rising social unrest driven by growing inequality and poverty, which is being manifested by large labor unions and organizations. Additionally, Milei’s lack of political support and representation in Congress and among provincial governors has become a significant obstacle, preventing Milei from passing many of his reform bills. 

Historically, in Argentina, once social rest reaches a certain point it has often led to governmental overthrows and cyclical periods of political dissatisfaction. Notably, during Macri’s presidency in 2015 and 2019, his attempt to reshape the economy through neo-liberal reforms fell short because of growing discontent from the public. This goes to show that the public won’t tolerate these conditions for too long, and so Milei’s government has to soon address the growing inequality and poverty in Argentina. This is increasingly important as, according to most polls, inflation has dramatically declined as the main problem identified by Argentines and has been replaced by fears of job loss and poverty (Murillo). This shift in public concerns signals that Milei’s policies have been a double-edged sword, as his reduction of social spending has caused high levels of social unrest- including two general strikes and one large protest (Murillo).

On another note, to expand his political influence, Milei could also benefit from forming political alliances with the Peronists and the political party Pro. A trend amongst the interviewees is that most of them agreed on the fact that Milei has to make more of an effort to collaborate with other political parties to gain broader support. The political party Pro, previously led by former president Macri, makes up a wide proportion of Milei’s voter base. Macri’s backing, along with support from Peronists, would be highly valuable for Milei, as Macri’s political coalition, Juntos por el Cambio, and Peronists hold a substantial number of seats in both the Chamber of Deputies and the Senate. This would enable Milei to gain greater legislative influence (Centenera 2024). Although their backing may come at the cost of ‘watering’ down some of Milei’s radical proposals, in the longer run, this political support would ultimately reduce the need for extensive negotiations as Milei gains political support over time. 

Given the macroeconomic success Milei has achieved so far, his government appears to be focusing most of its resources on supporting those in the private sector and fostering business growth, which is argued is necessary to stabilize key macroeconomic variables. However, while continuing to manage Argentina’s macro economy, Milei could allocate some of these resources to alleviate the challenges faced by various sectors of Argentine society. By doing so, he could strengthen political backing from Peronsits and other center or left-leaning political groups, while also reducing the risk of losing some of his existing support. The conventional view of Milei is that he is a ‘crazy, madman.’ Pordalenne states, but “perhaps we needed a profile like that to make a change as the one that is being experienced, a change so strong to get Argentina out of its misery.”

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About the author

Olivia Knoell

Olivia is a senior at The American School of Madrid. She plans to study Political Science in college, with a particular focus on political economies in Latin America. In this research paper, she explores Argentina’s current political and economic transition and Javier Milei’s public policies in response to this shift. She conducted primary research in Argentina by interviewing key political figures and opinion leaders in Buenos Aires over the past summer. The interviews Olivia conducted included heads of government secretariats, union leaders, NGO directors, and Argentine professors who specialize in political science.

The post Javier Milei’s Neo-Liberal Vision: Sources of Support and Resistance in Argentina appeared first on Exploratio Journal.

“Growing up, I remember politicians hopping on TV to talk about how they would save the cities from the ‘menace’ of drug traffickers. It was the age of the ‘super predator’ and we were all supposed to be grateful for leaders who prioritized law and order. But I didn’t know any super predators.”

— Mikki Kendall, Hood Feminism: Notes From The Women That A Movement Forgot

Before Nixon and Reagan’s war on drugs from the telly was Harry J. Anslinger’s announcement on the radio, telling Americans that they should “beware!” of marijuana, and telling parents that the youth are increasingly “…continuing addiction until they deteriorate mentally… and turn to violent crime and murder.” Anslinger was the “drug czar” of his time, and was appointed first commissioner of the Federal Bureau of Narcotics (FBN) in 1930 by President Herbert Hoover, which was the precursor of the DEA. His efforts to incite panic in the American population about the effects of drugs and to shut down medical experts made him the father of American drug policy.

His impact never left the country’s policies. On June 17th, 1971, Richard Nixon first introduced to the Washington Press Corps the urgent need for a war on drugs, making “this statement, which I think needs to be made to the Nation: America’s public enemy number one … is drug abuse. … It is necessary to wage a new, all-out offensive.” This was primarily supplemented by the creation of the Drug Enforcement Agency (DEA). Eleven years later, on October 14th, 1982, President Ronald Reagan, in an address to the nation from the Great Hall at the Department of Justice, declared there to be an “emergence of a new privileged class in America, a class ofrepeat offenders and career criminals … the result of misplaced government priorities and a misguided social philosophy. … This philosophy suggests in short that there is crime or wrongdoing, and that society, not the individual, is to blame.”

This declaration was backed by his eight-point-plan, which included unleashing 12 task forces to suppress organized involvement in drug abuse, “including the FBI, the DEA, the IRS, the ATF, Immigration and Naturalization Service, United States Marshals Services, the U.S. Customs Service, and the Coast Guard,” as well as the creation of a center for training local law enforcement in combating drug smuggling and other syndicated crime, and the allotment of millions of dollars to prisons and jails for their expansion.

The War on Drugs is the usage of greater punishment and legal enforcement against drug use in the United States. The greater rise of the War came in tandem with the rise of President Reagan’s Neoliberal policies (“Reaganomics”) that pushed off from New Deal Liberalism, which included tax cuts, diminishing market regulation, and allowing for free trade, as well as low social welfare spending. Such policies fall under one principal theme: minimal government accountability for the benefit of American communities. Instead of Reagan himself, the visibility of the War shifted to TV programs and sensationalistic news stories. The Reagan era (1981-1989) War on Drugs shattered communities using ‘attack’ policies and media propaganda to create a cultural frenzy of fear of drugs and drug users. What could have been an attack on addiction was instead a war on people; war, in any form, of course, does nothing but cast a long-lasting, haunting specter over a country.

Harry Anslinger’s Marijuana Fairytales

Harry Anslinger’s descriptions of Communist Chinese men luring white women into their ‘opium dens’ led to official raids on Chinese communities. Newspaper tales of the special potential violence of black Americans on cocaine— while Anslinger stressed that black Americans already made up ‘60 percent of the addicts’— had Southern police increasing the caliber of their guns. The violence enacted against people of color was not a side effect– the point of the aggression was about putting them in their place, lest they, to Anslinger’s imagination, got too excited on dope and cocaine and infiltrated or violated white society. George White, Anslinger’s favorite agent from the FBN, complained about Billie Holiday’s “fancy coats and fancy automobiles and her jewelry and her gown,” before seemingly planting a heroin kit and opium in her hotel room in San Francisco. Holiday got charged with possession, but upon interrogation from journalists about the unlikely location of the stash (a wastepaper basket), White could only stammer.

Anslinger did not come across such egregious falsehoods about narcotics and marijuana from unintentional ignorance. He did not believe marijuana to be a major issue until he began to envision Mexican Americans and African Americans, gorged on drugs, laying hands on naive white girls. He then inquired about the effects of marijuana to thirty scientific experts. All thirty wrote back. Only one called to ban it, which was the only confirmation he needed. He continued to proclaim to the public stories of marijuana turning normal citizens into crazed killers. Doctors came to him with evidence that, perhaps, marijuana made one sleepy at most, or that it was not an inherently evil substance in general, and in return, Anslinger refused to ever fund independent scientific research.

When Edward Huntington Williams, a doctor who was a leading expert on opioids, and an extremely highly regarded expert in medicine, opened a free clinic to prescribe drugs to addicts, he was only following tradition before the reign of Anslinger. Local pharmacists, at low prices, sold remedies with morphine and heroin as casually as those today might sell sugary cough syrups. Regular users would carry on working and raising their families, even among the ones who got hooked. Once the crackdown on narcotics administered by doctors began, he saw “tens of thousands of people, in every walk of life, frantically craving drugs that they could in no legal way secure… must have known that their Edict, if enforced, was the clear equivalent of an order to create an illicit drug industry.” But he would soon be destroyed by Anslinger’s second poison, if the first one was media weaponization, and the second was unleashing his zealous agents onto civilian territory. A decoy addict stumbled into his clinic, and Williams, having no reason to fear a consequence, wrote him a prescription of heroin to get him back to normal. He would become one of twenty thousand doctors busted by police, and his life work became obsolete. These ‘poisons’ would remain the main two weapons decades in the future.

This was the basis of the Drug War— Anslinger’s fear isolated every drug user from their supply, artificially creating a drug problem as people no longer had a way to get a safe supply. What were the long term implications? Just as the prohibition-era Mafia sold alcohol because of the market placed directly in their hands, drug peddlers and cartels now had something that people wanted badly.

Reagan’s Imagination and the American Culture Dish

Ronald Reagan, even during his time only as a Hollywood actor, expressed, in a 1957 speech at his alma mater, Eureka College, dismay over Americans, under New Deal regulated Liberalism, being crushed “into a mold of standardized mediocrity,” as there was a seemingly economic minimum and maximum a person could be a part of. In Reagan’s mind was the vague, glamorous and heroic age of capitalism, where “American pioneers” risked their own souls in the market and could either fall into poverty, or be launched into prosperity, based all on their hard work.

During his presidency, he pointedly avoided enacting policies which could have built off of 1960s-era domestic policies (such as Medicaid and Affirmative Action) by not addressing poor housing conditions, failing education systems, and unemployment. Instead, under the Reagan administration, half a million families were stricken from the welfare eligibility, one million people from food stamps, over two-and-a-half million children from school lunch programs; he introduced nine block grants in 1982, reducing the money the federal government could allot to the states and, in general, central government accountability — inflation increased, for the government turned a blind eye, and Americans got poorer.

Famously, in the years leading up to his presidency, before losing his presidential nomination to Gerald Ford, Reagan painted the picture of the morally corrupt Linda Taylor, who would come to use “eighty names, thirty addresses, fifteen telephone numbers to collect food stamps, Social Security, veterans’ benefits for four non-existent deceased veteran husbands, as well as welfare,” opening the floodgates to a barrage of media coverage on welfare and Medicaid cheats, as Reagan himself bemoaned the apparently fraud-ridden system. “Only our deep moral values and strong institutions can … restrain the darker impulses of human nature,” Reagan told the International Association of Chiefs of Police in 1981. In early January of 1967, in his Inaugural Address as Governor of California, he called welfare money an inhuman destruction of “self-reliance, dignity, and self-respect,” and the maker of continued poverty. He quoted Benjamin Disraeli, a mid-19th century Prime Minister of the United Kingdom, with “[m]an is not a creature of circumstances. Circumstances are the creatures of men.” This statement would be followed by his formerly noted lack of ‘circumstance’ shifting for poverty, only attacking the symptom of poverty, which is, of course, the welfare system.

The anti-welfare panic among Americans (84 percent of Illinois voters considered welfare a prominent concern in 1978) neatly set up the culture that would lead to another kind of moral outrage.

The Television and Cop Love Affair

Children in school in the U.S. would learn to become familiar with the state mandated, seemingly benign, and all important message to “Just Say No!” to drugs, with First Lady Nancy Reagan’s face at the forefront of it all in talk shows and television news programs. This was one key strategy of the Reagan administration’s crackdown: with the assertion blazing across TV programs (notably Diff’rent Strokes (1978) and Punky Brewster (1984)), covers of arcade games, and other widespread educational programs to say no to drug use, Americans were sure to get the message that the drug issue was the issue caused by the irresponsible and the demoralized; that is, because too few people were saying ‘no.’

In The New Jim Crow, Michelle Alexander notes that in August of 1986, Time magazine called crack the “issue of the year,” and the thousands of crack stories that featured black faces represented these crack users in various headlines and stories. With Linda Taylor already the face of the corrupt and lazy ‘welfare queen,’ the crack sensation only solidified placing black communities and people as the target of the war. Politicians from both parties were pushed to be ‘tough on crime’ in policy. It became the universal, unquestioned stance. Who would be against preventing American children from getting drugged up by the dregs of society?

The media and the police were becoming increasingly intertwined. Cracked Coverage by Jimmie Lynn Reeves brought forth the imagery of TV cameras doggedly following cops as they bust into houses for their drug raids. Geraldo Rivera, an American political commentator, had a 1986 program called “American Vice: The Doping of a Nation” which included clips of real police drug raids. A Time article commented that “antics of Rivera’s show highlighted concerns about the increasingly common practice of letting TV crews tag along on drug raids.” The citizen exploitation by broadcasting images of their wrongdoing and downfall was an important aspect to how the War invaded the American psyche. Rivera’s show echoed Miami Vice, a popular adventure cop show of the time, by name, attaching an embarrassment of drama to what should have been a dull crime. The program was controversial, but the sensationalism it indulged in was not new in the journalism world. In the 1991 study “Negotiating Control: A Study of News Sources” by Richard Ericson, Patricia Baranek, and Janet Chan, the extent to which the media became not just a puppet of the militarization of the War, but working right alongside the police was commented on:

The police have come to appreciate that the news media are part of the policing apparatus of society, and can be controlled and put to good use in this respect. The news media are incorporated into the architecture of new police buildings (they are given newsroom facilities there), they are taken into account in police organizational charts, they are subject to the regulations in police operation manuals, and they are part of everyday practice at all levels of the police hierarchy.

What made the Reagan era of the War unique was this, then; the transition from the image of a mere public crime crisis in certain (urban) areas to being a scandalous specter— white powder that could coat even the neighborhoods of suburban middle America. Had the issue remained the first, it would have been ignorable to many, a problem that merely plagued the unfortunate peoples in impoverished parts of the city.

It was not just crack stories hitting the news seemingly each day that defined the uniquely severe push of the Reagan Era’s War, but of course, Nancy’s Just Say No campaign that allowed her to transform into an American figure of benevolence. Drugs were a product of a kind of unnatural urban hell that could ‘infiltrate’ neighborhoods through malevolent or ignorant outsiders, and suburban American neighborhoods self-fortifying against this threat, creating self-contained communities which emphasized nuclear family values, became another key Reaganist idea. The “community mobilization” of these neighborhoods took the form of a “society of informers.” Christina J. Johns, in “Power, Ideology, and the War on Drugs: Nothing Succeeds Like Failure,” notes the “relish” in which society began turning in those within their communities: “In a poll conducted by the Washington Post/ABC News in 1989, 83 percent of the respondents favored encouraging people to phone the police to report drug users even if it meant turning in ‘a family member who uses drugs.’” This extended into schools. The Drug Awareness Resistance Education (DARE) program, as administered by the LAPD, and founded in 1984, planted uniformed police officers in classrooms to tell children to “Just say no,” and additionally to tell on their peers who might be saying ‘yes.’ ‘DARE Boxes’ were installed in some classrooms where the students could act on DARE’s Three R’s— “Recognize, Resist, and Report”— and put in the name of a suspected drug-using peer. This, alongside the police hotlines placed everywhere, boosted by local news organizations, created an environment of total fear and lack of trust. What were the effects on children from uniformed men knocking down doors on screen as national heroes dragging out the undesirables of society, and those same men coming to their schools to teach them that some of their own peers and even parents could be one of them if they were not careful enough?

Reaganist Policies

Before the Anti-Drug Abuse Act of 1986, which codified criminal penalties for possession of a controlled substance, increased penalties for minors in a drug business, and the expansion of mandatory minimum penalties, there was the 1984 Comprehensive Crime control Act of 1984, which established those mandatory minimum sentences. That same year, the DEA launched Operation Pipeline, based on drug traffickers’ usage of U.S. highways to move their wares around. This is a federal program that mainly trains officers specifically to use pretextual and ‘consent’ searches on the road for mass seizure of drugs and arresting of those possessing drugs in their vehicles. Of course, those most really affected by the “war” were never large scale distributors or kingpins. Four out of five drug arrests were for mere possession, and the other fifth were for selling.

During Reagan’s second term, the Anti-Drug Abuse Act of 1986 was launched. It doubled the level of money already given to domestic crime and drug restraint initiatives during his first term, and tripled drug enforcement funds. While Reagan’s speech on it seemed to emphasize drug treatment and education, “the $900 million allocated by Congress for drug abuse programs … went mostly for the purchase of helicopters, airplanes, and intelligence-gathering facilities.”

It is true that some attention was given to schools, however, as the Drug Policy Board facilitated drug testing sites in workplaces and rescinded federal student loans if the student was convicted of a drug offense (which included simple drug use).

The aim of the game under Reagan’s rule was less government accountability, which was achieved by the false utopian racial ideal America had convinced itself of embodying: color-blindness. Reagan himself commemorated Martin Luther King Jr.’s judge-not-by-color-of-skin line, and then commemorated his upholding of the ideal, citing record amounts of black Americans holding jobs in 1984. Of course, claiming colorblindness in a society and individualizing the drug issue, as the Reagan Administration did, erased the need to address impoverished conditions that might lead to crime to increase the possibilities of literal survival, and also erased the need to examine possible racial profiling and otherwise unfairly motivated officers (such as by property and money seizures and federal funding that came from each drug user caught).

Conclusion

What was left in the United States was a culture of baseless individualization and the gross entanglement of the media and police organizations. The isolation of drug users and sensationalized cocaine and weed use continued and perpetuated a legacy from the 1930s, and was exacerbated by bringing cops into people’s homes and schools, creating a state of fear and discord within American communities: the War on Drugs being a war in this way doomed its goal of a better America from the start. What’s the alternative? Anyone today can see that the ideas fabricated during the 1930’s, 1970’s, and 1980’s are still often considered ‘common sense.’ People know about the big cartels, imagining waterfalls of pills pouring into the country, and into the hands of middle-market sellers. They know that they land in the hands of drug retailers haunting the shadows of the neighborhoods they know to drive quicker through. They shall advise children to ‘just say no,’ of course.

They are not wrong. But it is a shallow perspective, one that is still unchallenged in our higher offices, by politicians who know it is generally advisable to declare toughness on crime and ‘clean the streets.’ The philosophy— if one could call it that— from decades ago is as strong as ever. Those who wish to reject the common pessimism will talk about improving living conditions: wages, access to food, home life, even. While the quality of life is nearly without exception a major consideration to make when analyzing any shade of societal ‘bad behavior,’ it would be shrewder to remember how life was before any of this ever began: back in the early nineteenth century, when drug use was nowhere near as pathological, and therefore almost never life-ruining. Instead of rehabilitation centers running on the attitude of tough love at best, which remove any source of comfort in an effort to bring addicts back to reality, cities should consider opening drug clinics for those who cannot function in their life properly any longer, and let them receive doses as they wish, so they a) do not have to act destructively to themselves or people around them in order to obtain the drugs and b) can self regulate to low doses once more. People do not have to be alone and rejected from their communities, indeed, the thing most important for a human being to be a part of.

Bibliography

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About the author

Celine Guay

Celine is a senior at the San Francisco University High School. She has a deep passion for history and hopes to study political science in college to make the world a better place than when she found it – but besides that, she likes to read, act in shows, and hang out with her cat, Cheerio.

The post Snow Ghosts and Marijuana Fairytales: How Reagan’s Americans Embraced the War on Drugs appeared first on Exploratio Journal.

Abstract

This literature review examines various programs in Guatemala and internationally that address malnutrition, aiming to identify potential improvements for the “Centros Comunitarios de Desarrollo Infantil Integral” (CECODII). With malnutrition causing significant health issues and developmental delays in Guatemalan children, finding effective solutions is crucial. The review evaluates key Guatemalan programs, such as CECODIIs, Hermano Pedro, and Centros Nutreme. It compares them with international initiatives like India’s Integrated Child Development Services (ICDS) and Niger’s Millennium Development Goals (MDG) fund. While CECODIIs focus on early childhood development, the Centros Nutreme program offers a holistic approach that includes family support and self-evaluation.

Similarly, international programs demonstrate the effectiveness of integrating child and family support services. The review suggests that CECODII centers could improve outcomes by adopting holistic, family-focused strategies and implementing regular evaluations. By allocating more governmental funds and learning from successful international models, CECODIIs can enhance their impact on combating malnutrition in Guatemala.

Introduction

Malnutrition remains a pervasive issue in Guatemala, contributing to developmental delays, weakened immune systems, and a significant portion of child mortality. In particular, malnutrition is responsible for 45% of deaths among children under five and poses long-term challenges that perpetuate across generations (Ellie Ostvig). Despite numerous efforts, the high rates of undernutrition persist due to factors such as food insecurity, economic instability, and climate change. Addressing this complex problem requires a comprehensive and effective strategy.

This literature review explores various programs within Guatemala, such as the CECODIIs and Centros Nutreme. It examines international efforts, including those in India and Niger, to identify potential improvements for Guatemalan initiatives. By understanding the strengths and weaknesses of these programs, this review seeks to provide actionable insights for enhancing the effectiveness of the CECODII centers.

The CECODII centers in Guatemala, supported by government funding, focus on child development through the “Acompañame a Crecer” program. Meanwhile, the privately operated Centros Nutreme offers a more integrated approach, supporting not just children but entire families. International models like India’s Integrated Child Development Services (ICDS) and Niger’s Millennium Development Goals (MDG) fund offer valuable lessons in holistic programs design and implementation. Through this comparative analysis, the review aims to propose strategies that could bolster the efforts of CECODIIs, thereby improving the nutritional and developmental outcomes for children in Guatemala.

Programs in Guatemala

CECODII Centers

The CECODIIs in Guatemala implement the “Acompañame a Crecer” curriculum, which ensures children’s rights for those aged 0-4 and increases family involvement. The program, supported by the Ministry of Education (MINEDUC), Ministry of Health (MSPAS), Ministry of Social Development (MIDES), Ministry of Agriculture (MAGA), Ministry of Environment and Natural Resources (MARN), and Secretary of Food and Nutritional Security (SESAN), emphasizes initial education, health, nutrition, security, and protection. Activities include teaching mothers how to improve their children’s cognitive development with age-appropriate exercises. In 2023, the government invested 7.38 million Quetzales in CECODIIs, impacting 47,448 children and assisting 32,582 mothers. However, the budget for meals is only 4 Quetzales per person daily (0.5 US dollars), suggesting economies of scale may be necessary to maximize resources.

Centros Nutreme

Centros Nutreme is a privately run program using the “Acompañame a Crecer” curriculum. It supports pregnant and lactating women and children aged 0-4, offering home visits for younger children (0-2) and group programs for older ones. The program includes sanitation, family economy, and farming components to enhance overall family well-being. It provides families with resources like filtered water and home gardens, and empowers women as “Madres Educadoras”. Centros Nutreme also evaluates its programs using tools like “Carnets”, which hold the child’s information and track the child’s growth. By using tools similar to this one, the centers can adjust strategies as needed.

Hermano Pedro

Hermano Pedro approaches malnutrition through the clinical treatment of chronic cases. The program includes physiotherapy, neurological care, and volunteer-led activities for children, while parents receive therapy to cope with separation. Post-discharge, children are monitored monthly, and parents attend workshops on nutrition and sanitation. While not as comprehensive as Centros Nutreme in family support, Hermano Pedro emphasizes the importance of family health.

International Programs

ICDS – India

Launched in 1975, the Integrated Child Development Services (ICDS) in India aims to foster proper psychological, physical, and social development, reduce child mortality and malnutrition, and enhance maternal capabilities. It offers supplementary nutrition, immunization, health checkups, referral services, preschool education, and health education for children and mothers. Like Centros Nutreme, ICDS takes a holistic approach, supporting families in multiple areas. This program did have a positive impact on the population but still had 14.9% of children who were underweight and 13.5% of moderately unnourished children (Chudasama et al., 2016). These results are due mostly to the variance of services and programs offered in the different centers. Only 46.9% of the centers were offering food and only 64.6% had a separate toilet facility, these inconsistencies can greatly affect the result of the program (Chudasama et al., 2016). This is why it is so important to have standardized programs, if the program is not standardized there will be gaps created in the program, and it will not have the results it should produce.

MDG Fund – Niger

The Millennium Development Goals (MDG) fund in Niger targets nutrition for children aged 6-59 months, food security, and education. It provides nutrition for young children, training for teachers on nutrition, and support for pregnant women. The program also introduces nutrition topics in schools and trains health workers and community volunteers, emphasizing a comprehensive approach to family health. Due to this program over 102,000 children under 2 years old received treatment for undernourishment; 400 teachers have been trained on nutrition and training modules are being implemented in primary schools; More than 52,500 pregnant women in the department of Mirriah received antenatal care and around 8500 had assisted deliveries. The Essential Family Practices module increased the use of basic social services and increased consumption of vegetables from school and community gardens; training was conducted for the district management team, health workers of integrated health centers, and community volunteers; and women’s knowledge about contraceptive methods was increased (Childhood, Food Security and Nutrition Programme | MDG Fund).

Conclusion

Comparing these programs reveals that successful initiatives address both child development and family well-being. Programs like ICDS and the MDG Fund demonstrate the importance of holistic support, while Centros Nutreme and Hermano Pedro highlight the value of monitoring and evaluation. To enhance CECODII centers, integrating family-focused services and regular evaluations is essential. Providing holistic support to the participating families will allow the child to not only have improved development through the program but to have a stable family with a secure income. The regular evaluations will allow the program to be consistent and have the results they seek. Increased funding also ensures adequate nutrition and resources, which are crucial to providing a holistic program and leading regular evaluations. By learning from successful international programs, CECODIIs can improve outcomes for Guatemalan families.

Works Cited

1. Centro de Recuperación Nutricional – Obras Sociales Del Hermano Pedro. Website. Accessed 3 July 2024.
2. Childhood, Food Security and Nutrition Programme | MDG Fund. Website. Accessed 3 July 2024.
3. Denisse Mabel Alemán Vargas. “Guia Para La Atencion De Los Niños 0-1 Años.” Google Docs, 2020. Document.
4. Denisse Mabel Alemán Vargas. “Guia Para La Atencion De Los Niños 1-2 Años.” Google Docs, 2020. Document.
5. Ellie Ostvig. “Undernutrition Among Children in Guatemala.” Ballard Brief, 2023, https://ballardbrief.byu.edu/issue-briefs/undernutrition-among-children-in-guatemala.
6. Integrated Child Development Services – ICDS Scheme | Ministry of Women & Child Development|IN|tngkr. Website. Accessed 3 July 2024.
7. Puente | Centros Nútreme. Website. Accessed 26 June 2024.
8. SESAN. “Sesan Socializa Intervenciones En El Marco Del Programa ‘Acompañame a Crecer’ – Gobierno de Guatemala.” Website.
9. Tania Patricia Santisteban López. “Guia Para La Atencion De Los Niños 2-3 Años.” Google Docs, 2020. Document.
10. Tania Patricia Santisteban López. “Guia Para La Atencion De Los Niños 3-4 Años.” Google Docs, 2020. Document.
11. Undernutrition Among Children in Guatemala – Ballard Brief. Website. Accessed 12 Dec. 2023.Childhood, Food Security and Nutrition Programme | MDG Fund. http://www.mdgfund.org/program/nigerchildhoodfoodsecurityandnutritionprogram. Accessed 3 July 2024.

About the author

Marie Berthelemy

Marie is a high school student at Colegio Maya in Guatemala City. She is passionate about service learning and entrepreneurship and has been recognized for her academic achievements, including the Presidential Award for Educational Excellence, but her work with her non-profit, Niños por Guatemala, drives Marie the most. She has also co-founded a student-run coffee shop at her school, where the profits we make goes towards scholarships for the children of our school staff.

Beyond that, Marie stays active in sports, research, and culinary arts — she loves learning in different ways. Recently, she had the chance to work with the J-PAL representative on a literature review for the CECODII programs here in Guatemala, which was a great way to combine research with making an impact in her community.

The post Optimizing Early Childhood Development Programs: A Comparative Study of CECODII Centers and International Initiatives appeared first on Exploratio Journal.