Bibliographic record
Abstract
Resisting contemporary gendered expectations of slenderness is challenging, with many fat women feeling out of sync with hegemonic body standards. Cultural size norms for women have trended increasingly thinner in the current “Ozempic era” (Oswald 2024), reviving Y2K thin body ideals for women (Grose 2022). One notable site of protest is the wave of fat feminists online who have loudly rejected the cyclical New Year’s resolution mandate to perform a desire for weight loss. Expressing themselves in mediums from selfies to comics, many fat content creators like Mollie Cronin have begun proclaiming a future-focused anti-resolution: they are “Staying Fat In [Year]”. This serves as a visible “coming out” (Sedgwick and Moon 1993) and a meaningful counter to pro-diet content that often overwhelms social media algorithms at year’s end. We use Critical Technocultural Discourse Analysis (Brock 2020) to situate how these creators’ interventions use Instagram to intervene in capitalist diet rhetoric on social media. We draw on theorizations of fat temporality to argue that by rejecting size-based standards for heteronormative desirability, these creators queer “straight time” (Muñoz 2009), which assumes that all women are pursuing a “straight” linear temporal trajectory toward straight sizes as they continually strive to be seen as heteronormatively attractive. Fat feminist strategies like anti-resolutions work “against progress” (Fox 2018) to embrace a fat temporality where alternative futurities for our diverse embodiments are not only possible but valid (Yingling 2016). By publicly choosing sustained fatness, feminists are using their digital presences to visibilize new queer horizons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".