Sizing up gender: Bringing the joy of fat, gender and fashion into focus
Bibliographic record
Abstract
This photo essay explores the intersections of gender, fatness and fashion through an innovative and evocative arts-based methodology involving collaboratively constructed macro, or close-up, photographs, portraits and garment images. With these images, we can examine people’s experiences at the intersections of fat and gender through one of the most visible and embodied ways by which we construct and resist dominant narratives about these subject positions: fashion and self-fashioning. The Sizing Up Gender project engaged twelve self-identified cis-gender, trans, non-binary and two-spirit fat people across diverse race, class and other subject positions. Their narratives disrupt many dominant understandings of fat bodies and fashion and introduce a joyfulness to the story of dressing fat bodies that has been sorely neglected. We connect these feelings of joy to the concept of fabulousness, and consider how our participants’ experiences of joy and risk are not only due to genders, races and sexualities but also to how these identities intersect with their fat embodiments, fatphobia and weight stigma. The images presented here, particularly the macro photographs, force us to look more closely at the subject matter at hand and introduce a visual fabulousness of their own, a fabulousness that is rarely afforded to fat bodies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".