Thin for the Win: Aesthetic Bias and Body Image Dissatisfaction in Aesthetic Sports
Bibliographic record
Abstract
Les athletes evoluant dans des sports dits « esthetiques » tendent a etre predisposes a des biais de cette nature crees dans leur sport; par consequent ils seraient plus a risque de developper de serieux problemes de sante mentale tels qu’une insatisfaction face a leur image corporelle et des troubles de l’alimentation. Ce texte presente une discussion des facteurs qui creent ces biais chez des athletes dans ces sports esthetiques, les rendent plus a risque de souffrir de ces problemes de sante mentale et ce qui peut etre fait pour changer cette culture. Nous explorons la centration sur le physique dans ces sports et comment cette centration est en relation avec des normes et des problemes dans la societe, plus particulierement pour les athletes feminines. De plus, nous aborderons les avantages d’un corps plus mince dans de nombreux sports esthetiques, ou de tels ideaux tendent vers des extremes dangereux. Bien qu’il soit difficile de modifier des biais inherents dans un jugement esthetique, de tels biais peuvent etre remis en question par la creation d’environnements de developpement positif, et de facon plus large en reclamant un changement dans les sports esthetiques eux-memes. Mots-cles : biais esthetiques; sport esthetique; athlete; image corporelle; minceur.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".