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Self‐Presentation in Exercise Contexts: Differences Between High and Low Frequency Exercisers

2004· article· en· W2068936061 on OpenAlexaff
Klmberley L. Gammage, Craig Hall, Kathleen A. Martin Ginis

Bibliographic record

VenueJournal of Applied Social Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster UniversityWestern UniversityBrock University
Fundersnot available
KeywordsPsychologyExpectancy theoryPresentational and representational actingAnxietyImpression managementSocial anxietySelf-efficacyClinical psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

The present study investigated the relationship between cognitive manifestations of self‐presentation (social physique anxiety, self‐presentational efficacy, impression motivation, and exercise imagery) and exercise behavior in 235 female exercisers. Each participant completed the Social Physique Anxiety Scale, a measure of self‐presentational efficacy, the impression motivation subscale of the Self‐Presentation in Exercise Questionnaire, and the Exercise Imagery Questionnaire. The results of a MANCOVA indicated high‐frequency exercisers reported higher levels of efficacy expectancy, outcome value, and exercise imagery than did low‐frequency exercisers. Semi‐partial correlations showed efficacy expectancy, outcome expectancy, and appearance imagery each accounted for significant variance in social physique anxiety, independent of other predictors. Self‐presentational efficacy expectancy appears to be a potent variable in both exercise behavior and social physique anxiety.

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)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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

Citations70
Published2004
Admission routes1
Has abstractyes

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