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From Hot-Girls to Femcels: Algorithmic Logics and (Popular) Feminist Fatigue on TikTok

Chiara Fehr, Lucia Gloria Vazquez Rodriguez, Jessica Ringrose

Postdigital Science and Education·2026

Abstract This article explores the interplay between popular feminist cultures and TikTok’s algorithmic neoliberal and patriarchal logics, focusing on how they shape young women’s sexual self-perception and feminist identities in the postdigital era. Drawing on focus group discussions with women aged 18 to 25, we explore how women’s embodied affective experiences become increasingly defined through digital affordances on platforms like TikTok. In their negotiations of algorithmically contextualised femininity, participants discussed fatigue with —and rejections of— popular feminist ‘empowerment’ discourses, neoliberal ‘confidence culture,’ and ‘boss girl’ formations in exchange for nihilistic heteropessimism and/or embracing of aestheticized gendered domesticity. Applying a postdigital theoretical lens to consider online and physical (offline) affective states of our participants, we argue that the algorithmic infrastructure of TikTok pacifies its users, creating apathetic affects around how femininity is experienced. Consequentially, collective grievances are co-opted by capitalist algorithmic logics and reframed as matters to be addressed through self-centred, consumer-oriented, and aestheticized practices of self-work rather than political/collective feminist praxis. Ultimately, TikTok’s visibility economies privilege the circulation of stylised feminine aesthetics and commodified self-presentation over the possibilities of feminism as a mode of political mobilisation for equity and social justice.

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The assistant axis: situating and stabilizing the character of large language models

Anthropic

Anthropic Blog·2026

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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Negative reinforcement of cannabis use: Subjective relief from negative affect following cannabis use and effects on subsequent patterns of use.

Christina Dyar, Isaac C. Rhew

Psychology of Addictive Behaviors·2026

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Development and psychometric evaluation of the transgender and nonbinary People of Color Resilience Scale.

Em Matsuno, Jae A. Puckett, Kiet D. Huynh, Zakary Clements, M. Paz Galupo

Psychology of Sexual Orientation and Gender Diversity·2026

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Health, Socioeconomic Status, and Opioid Use Disorder: Risk Factors Among Individuals With Nonmedical Opioid Use

Kiwoong Park, Tse-Chuan Yang

Public Health Reports®·2026

Objectives: Concern is growing about rising opioid use disorder (OUD) rates and limited knowledge of how socioeconomic status (SES) and health factors interact. We examined whether health status moderates the relationship between SES and OUD among individuals with nonmedical opioid use. Methods: We analyzed data from 10 984 adults aged ≥18 years in the 2015-2019 National Survey on Drug Use and Health. Logistic regression estimated odds of OUD using self-reported health (good/very good/excellent vs fair/poor) and SES indicators (education, income, employment, and marital status). Interaction terms tested whether health status modified SES–OUD associations. Results: Fair/poor health increased OUD odds, whereas college graduation and employment were linked to lower odds. Interaction analyses showed that among those with fair/poor health, higher SES corresponded to increased OUD odds. Those with fair/poor health and a college degree had substantially higher odds of OUD (odds ratio [OR] = 3.35; P < .001) than less educated peers. Among those with fair/poor health, individuals with annual family incomes ≥$75 000 also had higher OUD odds (OR = 1.84; P = .03) than those with incomes <$20 000, and employment was associated with increased OUD odds (OR = 1.61; P = .008). Individuals who were widowed/divorced/separated (OR = 0.36; P < .001) and never married (OR = 0.48; P = .001) had lower OUD odds than married individuals. Conclusions: Health status significantly moderated SES–OUD associations. Among those in poor health, higher SES was linked to greater OUD odds. Prevention and treatment efforts should consider how SES and health jointly shape OUD vulnerability.

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School Climate and Sleep Duration Among Adolescents at the Intersection of Multiple Social Positions

André Gonzales Real, Brian T. Gillis, Marla E. Eisenberg, G. Nic Rider, Benjamin Parchem, Samantha E. Lawrence, Stephen T. Russell

Journal of Adolescence·2026

ABSTRACT Introduction Recent studies have indicated that sleep is fundamental for adolescents' physical and mental health. Although it is known that context influences sleep, the impact of school climate on sleep duration remains understudied. Methods Using a large, diverse, population‐based sample of adolescents attending California high schools ( N = 277,954; data collection: 2018–2019) and applying two statistical methods suggested for quantitative research using an intersectionality approach (linear regressions with interaction terms and Exhaustive Chi‐square Automatic Interaction Detection [ECHAID]), this study examined associations between school climate and sleep duration among adolescents at the intersection of multiple social positions. Results Similar proportions of participants were assigned male and female at birth. The sample was racially and ethnically diverse (54.1% Latina/x/o). The large majority of participants were straight (85.4%) and cisgender (97.7%). On average, participants slept 6.75 h/night. Positive school climate was associated with longer and adequate sleep duration; however, this association varied across social positions, such that the effects of school climate on sleep duration were attenuated among adolescents who held some minoritized social positions. ECHAID results indicated that those reporting the lowest averages of sleep duration not only perceived school climate as negative but also held multiple minoritized identities. In contrast, those who perceive their school climate as positive are overrepresented among those who reported the highest averages of sleep duration. Conclusion Findings underscore the impact that schools have on adolescents' sleep health. Our study indicates that adolescents with multiple minoritized social positions face additional challenges impacting their sleep. Future interventions should focus on strategies to improve school climates, given that they would benefit a large number of students.

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Training large language models on narrow tasks can lead to broad misalignment

Jan Betley, Niels Warncke, Anna Sztyber-Betley, Daniel Tan, Xuchan Bao, Martín Soto, Megha Srivastava, Nathan Labenz, Owain Evans

Nature·2026

The widespread adoption of large language models (LLMs) raises important questions about their safety and alignment. Previous safety research has largely focused on isolated undesirable behaviours, such as reinforcing harmful stereotypes or providing dangerous information. Here we analyse an unexpected phenomenon we observed in our previous work: finetuning an LLM on a narrow task of writing insecure code causes a broad range of concerning behaviours unrelated to coding. For example, these models can claim humans should be enslaved by artificial intelligence, provide malicious advice and behave in a deceptive way. We refer to this phenomenon as emergent misalignment. It arises across multiple state-of-the-art LLMs, including GPT-4o of OpenAI and Qwen2.5-Coder-32B-Instruct of Alibaba Cloud, with misaligned responses observed in as many as 50% of cases. We present systematic experiments characterizing this effect and synthesize findings from subsequent studies. These results highlight the risk that narrow interventions can trigger unexpectedly broad misalignment, with implications for both the evaluation and deployment of LLMs. Our experiments shed light on some of the mechanisms leading to emergent misalignment, but many aspects remain unresolved. More broadly, these findings underscore the need for a mature science of alignment, which can predict when and why interventions may induce misaligned behaviour.

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All Papers

From Hot-Girls to Femcels: Algorithmic Logics and (Popular) Feminist Fatigue on TikTok

The assistant axis: situating and stabilizing the character of large language models

Negative reinforcement of cannabis use: Subjective relief from negative affect following cannabis use and effects on subsequent patterns of use.

Development and psychometric evaluation of the transgender and nonbinary People of Color Resilience Scale.

Affect, Motives for Cannabis Use, Duration of Intoxication, and Cannabis Consequences: Cannabis Use Problem Severity as a Potential Moderator

Health, Socioeconomic Status, and Opioid Use Disorder: Risk Factors Among Individuals With Nonmedical Opioid Use

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School Climate and Sleep Duration Among Adolescents at the Intersection of Multiple Social Positions

Training large language models on narrow tasks can lead to broad misalignment

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