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Record W3168117561

Thin for the Win: Aesthetic Bias and Body Image Dissatisfaction in Aesthetic Sports

2021· article· en· W3168117561 on OpenAlex
Leah Whitten, Jason Holt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2021
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsAcadia University
Fundersnot available
KeywordsHumanitiesArtAthletesEthnologyPsychologySociology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.291
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it