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Record W2144835468 · doi:10.1080/10413200802575742

Exploring Women Track and Field Athletes' Meanings of Muscularity

2009· article· en· W2144835468 on OpenAlexaff
Amber D. Mosewich, Adrianne B. Vangool, Kent C. Kowalski, Tara-Leigh McHugh

Bibliographic record

VenueJournal of Applied Sport Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAthletesPsychologyTrack and field athleticsContext (archaeology)NegotiationPerceptionSocial psychologyField (mathematics)Developmental psychologySociology

Abstract

fetched live from OpenAlex

Women athletes often struggle with attaining the muscular body needed to compete successfully, while at the same time negotiating expectations to conform to a lean and toned ideal. The purpose of this study was to explore women track and field athletes’ meanings of muscularity. Four adult and four adolescent women participated in focus groups and one-on-one interviews, exploring issues surrounding the body, including ideals and expectations. The participants also took photographs to represent their perceptions of their own muscularity. Four themes emerged: (a) many faces of muscularity, (b) a blurred line between appearance and performance, (c) a culture of comparison, and (d) a journey towards self-acceptance. Muscularity was identified as a complex and context specific experience, reflecting the multiple meanings of muscularity and the periods of struggle on the journey towards self-acceptance of the body.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.052
GPT teacher head0.324
Teacher spread0.272 · 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 designQualitative
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

Citations78
Published2009
Admission routes1
Has abstractyes

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