Body Image and Eating Disorders among Sexual and Gender Minority Populations
Bibliographic record
Abstract
Abstract This chapter reviews current literature pertaining to body image and pathogenic eating practices among sexual and gender minority populations. The authors begin by detailing three dominant theoretical frameworks that have been used to particularize why some sexual and gender minority persons are at risk of body dissatisfaction and disordered eating—the minority stress model, sociocultural theory, and objectification theory—as well as the pantheoretical model of dehumanization. Then, to highlight dominant trends in the literature, the authors summarize narrative and meta-analytic reviews on body image and eating disorders that target gay men, lesbian women, bisexual persons, and trans persons. The authors conclude by detailing obstacles that prevent researchers from better grasping the corporeal psychology of sexual and gender minority persons. These obstacles include (1) inconsistent and ambiguous operationalizing of constructs such as the “gay community”—constructs that are often invoked to explain why sexual and gender minority persons are at risk; (2) reliance on outdated measures of sexual orientation; (3) the elision of bisexual persons in body image scholarship; (4) the limited attention that is paid to the variability existing within sexual and gender minoritized groups; (5) the absence of research focusing on the dynamics of intersectionality as they pertain to the body; and (6) the lack of studies conducted outside of the United States.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".