Fat femininities: on the convergence of fat studies and critical femininities
Bibliographic record
Abstract
What is the relationship between fatness and femininity? How do prejudices toward fat bodies (i.e., fatphobia) and femininity (i.e., femmephobia) intersect? How does scholarship on femininities converge with scholarship on fatness? And, what novel insights can be cultivated by putting the fields of fat studies and critical femininities into conversation? In this article, we explore these questions, arguing that fatphobia and femmephobia, as well as the dominant cultural framings of fatness and femininity, are inextricably intertwined. Specifically, we challenge femininity’s associations with superficiality and oppression, discussing instead the importance of intersectional and recuperative approaches to fat femininities. Accordingly, this article illuminates the complex relationships between femininity and fatness; how these relationships differ across intersectional axes of privilege and oppression; as well as the ways femininity and fatness – or, by extension, femmephobia and fatphobia – intertwine to create unique experiences of gendered embodiment. Ultimately, with this article, we advocate for the importance of exploring diverse fat feminine embodiments and the potential for critical femininities to transform how we think about and embody fatness.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.127 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".