“We’re categorized in these sizes—that’s all we are”: uncovering the social organization of young women’s weight work through media and fashion
Bibliographic record
Abstract
BACKGROUND: For decades, dominant weight discourses have led to physical, mental, and social health consequences for young women in larger bodies. While ample literature has documented why these discourses are problematic, knowledge is lacking regarding how they are socially organized within institutions, like fashion and media, that young women encounter across their lifespan. Such knowledge is critical for those in public health trying to shift societal thinking about body weight. Therefore, we aimed to investigate how young women's weight work is socially organized by discourses enacted in fashion and media, interpreting work generously as any activity requiring thought or intention. METHODS: Using institutional ethnography, we learned from 14 informants, young women aged 15-21, in Edmonton, Canada about the everyday work of growing up in larger bodies. We conducted 14 individual interviews and five repeated group interviews with a subset (n = 5) of our informants. A collaborative investigation of weight-related YouTube videos (n = 45) elicited further conversations with two informant-researchers about the work of navigating media. Data were integrated and analyzed holistically. RESULTS: Noticing the perpetual lack of larger women's bodies in fashion and media, informants learned from an early age that thinness was required for being seen and heard. Informants responded by performing three types of work: hiding their weight, trying to lose weight, and resisting dominant weight discourses. Resistance work was aided by social media, which offered informants a sense of community and opportunities to learn about alternative ways of knowing weight. However, social media alleging body acceptance or positivity content often still focused on weight loss. While informants recognized the potential harm of engagement with commercial weight loss industries like diet and exercise, they felt compelled to do whatever it might take to achieve a "normal woman body". CONCLUSIONS: Despite some positive discursive change regarding body weight acceptance in fashion and media, this progress has had little impact on the weight work socially expected of young women. Findings highlight the need to broaden public health thinking around how weight discourses are (re)produced, calling for intersectoral collaboration to mobilize weight stigma evidence beyond predominantly academic circles into our everyday practices.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".