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Record W2209277728 · doi:10.1080/10640266.2015.1123986

Feminist identity, body image, and disordered eating

2015· article· en· W2209277728 on OpenAlexaff
Hannah M. Borowsky, Marla E. Eisenberg, Michaela M. Bucchianeri, Niva Piran, Dianne Neumark‐Sztainer

Bibliographic record

VenueEating Disorders · 2015
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsIdentity (music)Disordered eatingGender studiesFeminismPsychologyEating disordersGender identityFeminist theorySocial psychologySociologyDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

Using data from a community-based sample (Project EAT-III), this study (N = 1241; mean age = 25.2) examined the relationship of feminist identity with body image and disordered eating. Feminist-identified women reported significantly higher body satisfaction than non-feminist women and women who did not identify as feminists but held feminist beliefs. However, feminist-identified women did not differ from non-feminist women in disordered eating. Women holding feminist beliefs and non-feminist women did not differ in body satisfaction. Our findings suggest that self-identification as a feminist may promote positive body image in young adult women, but may be insufficient to change behaviors.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.339
Teacher spread0.313 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2015
Admission routes1
Has abstractyes

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