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Record W128342018

Training terminology on perceptions of women who engage in muscle strengthening activities

2013· book· en· W128342018 on OpenAlexaff
Brittany Cooper

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2013
Typebook
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTrainerPsychologyTerminologyPersonalityPerceptionImpression formationSocial psychologyApplied psychologyDevelopmental psychologyClinical psychologySocial perceptionComputer science
DOInot available

Abstract

fetched live from OpenAlex

The present study examined the impact of muscle strengthening terminology on impressions formed of female exercisers, in addition to the influence of participant impression motivation and BSRI category on ratings of personality and physical attributes. Male and female participants ( N = 265, M age = 21.23) were presented with one of four vignettes describing a female target (weight trainer, resistance trainer, strength trainer, control). Participants then rated the target on personality and physical characteristics. Results indicated no significant differences among ratings of target types ( p > .05). Moreover, the participants' impression motivation did not influence target ratings ( p > .05). A significant main effect emerged for BSRI category ( p < .05). Participants classified as masculine-typed rated all targets as less kind compared to participants classified as feminine-typed or androgynous. It is possible the vignettes did not provide enough information about muscle strengthening to elicit stereotypes. Avenues for future directions are discussed.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.266
Teacher spread0.216 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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