Obesity, bodily change and health identities: a qualitative study of Canadian women
Bibliographic record
Abstract
Medicalised concerns about an obesity crisis persist yet more needs to be learnt about everyday orientations to weight (loss). This article reports and analyses data generated using qualitative methods, including repeated interviews and fieldwork conducted over one year in Canada with women (n = 13) identifying as (formerly) obese. Three ideal types are explored using empirical data: (1) hopeful narratives; (2) disordered eating distress; and (3) weight-cycling or stagnation. Core themes include women's desire to embody a thin(ner) future and the good life, the harms of intentional weight-loss, and resignation to living as a fat woman whilst nonetheless challenging stigma. The article contributes to critical studies of weight/fatness, the sociology of bodily change and the embodiment of health identities. In concluding, we call for reflexive change in bodies of health knowledge, policy and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".