Externalized self-perceptions, self-silencing, and the prediction of eating pathology.
Bibliographic record
Abstract
Les modeles de socialisation associes aux roles sexuels proposent que le taux eleve de problemes alimentaires chez les femmes pourraient etre causes par un processus de socialisation specifiquement feminin. Ce processus encourage les femmes a se percevoir de facon relationnelle, a eviter les conflits et a se conformer aux normes sociales concernant la minceur. Cette etude examine le role de ce processus de socialisation dans le developpement des pathologies alimentaires. Deux cents trente-six etudiantes universitaire de premier cycle ont complete des mesures d'habitudes alimentaires, de variables corporelles (satisfaction corporelle, poids, attitude face au poids), et de style relationnel (orientation externe a la perception de soi, et effacement personnel). L'importance accordee a l'apparence et au poids, et une orientation externe a la perception de soi sont relies aux cognitions pathologiques face a l'alimentation. L'effacement personnel est relie aux comportements boulimiques lorsque les variables corporelles sont controlees. Les processus de socialisations feminins tels que l'effacement personnel et l'orientation externe a la perception de soi pourraient contribuer au developpement de desordres alimentaires. l'ar consequent, ces elements pourraient etres integres aux interventions psychoeducationelles et cognitives-comportementales.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".