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Record W2742775715

Body image, physical activity, and viewing patterns of physique images among women

2015· article· en· W2742775715 on OpenAlexaff
Garcia Ashdown‐Franks, Catherine M. Sabiston, Holly S. Howe, Afshin Aheadi, Timothy N. Welsh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychosocialPsychologyAffect (linguistics)Ideal (ethics)GazeSelf-imagePerceptionSocial psychologyHuman physical appearanceDevelopmental psychologyCommunication
DOInot available

Abstract

fetched live from OpenAlex

Exposure to media images of thin-ideal female physiques is linked to numerous maladaptive psychosocial outcomes, yet factors associated with propensity for media exposure have not been identified. This study examined the gaze patterns of women who were implicitly exposed to images of thin-ideal and average weight female models, and to test if behavioural (physical activity) and personal (body image and affect) factors differentiate viewing patterns. Healthy weight females (N=32) completed a computer-based experiment and self-report questionnaires on affect, perceptions, and behaviour. In the laboratory, one calendar depicting a thin-ideal female model and another depicting an average-weight female model were placed on either side of the participant. Unknown to participants, cameras recorded their gaze behaviour. The number of looks at each image was analyzed. A MANCOVA controlling for ethnicity and propensity for social comparison revealed that physically active women gazed at the average-weight image relatively more than at the ideal image, whereas inactive women did the opposite (physical activity x image interaction, F(1,28)=4.36, p

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.033
GPT teacher head0.324
Teacher spread0.291 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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