Rethinking Eating Pathology: Shifting the Focus from Gender to Body Ideals
Bibliographic record
Abstract
Traditional understandings of eating pathology (EP) have been constrained by stereotypes framining EP as a gendered phenomenon, predominantly shaped by years of research on body ideals based on gender—thinness for women and muscularity for men. This has led to a focus on gender and gender-typical body ideals in the development of EP-related etiological models such as the tripartite influence model. This thesis challenges the conventional exploration of EP based on gender and gender-typical body ideals. Instead, it explores EP based on body ideals without explicitly linking these ideals to any specific gender. Manuscript 1 reviewed the operationalization of intersectionality in EP research and noted limited theoretical engagement, gender binarism, and lack of incorporation of diverse identities and systems of oppression in intersectionality-informed EP studies. Manuscript 2 developed questionnaire items that assess body ideals while avoiding traditional gender stereotypes. The first item assesses body ideals with gender-neutral response options alongside binary gender-specific terms in brackets for clarity. The second item focuses on thin and muscular body ideals to generate data comparable to existing EP measures, which was used in Manuscripts 3 and 4. Manuscript 3 assessed the psychometric properties of the Sociocultural Attitudes Towards Appearance Questionnaire-4 Revised (SATAQ-4R) among thin- and muscular-oriented young adults in Canada. Results demonstrated robust validity and reliability of the SATAQ-4R in our samples. Manuscript 4 tested the tripartite influence model of EP among thin- and muscular-oriented young adults in Canada while incorporating gender, race/ethnicity, sexual orientation, and perceived weight status in the model. The models for both samples demonstrated acceptable-to-good fit, supported the categorization of individuals based on body ideals in understanding EP and provided partial support for the inclusion of social identities in the model. This thesis challenges the traditional focus on gender and gender-typical body ideals in EP research. Future research should aim to incorporate larger and diverse samples regarding gender and body ideals. Furthermore, examining intersections among social identities within the tripartite influence model will be crucial for better understanding of how multiple intersecting identities shape EP within the model.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.008 |
| 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".