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Record W2948534655 · doi:10.1123/apaq.2018-0066

Exploring Stereotypes of Athletes With a Disability: A Behaviors From Intergroup Affect and Stereotypes Map Comparison

2019· article· en· W2948534655 on OpenAlexaff
Rachael C. Stone, Shane N. Sweet, Marie-Josée Perrier, Tara K. MacDonald, Kathleen A. Martin Ginis, Amy E. Latimer‐Cheung

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsMcMaster UniversityUniversity of British ColumbiaMcGill UniversityQueen's University
Fundersnot available
KeywordsPsychologyCompetence (human resources)PerceptionAffect (linguistics)Elite athletesDevelopmental psychologyAthletesSocial psychologyClinical psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Identifying as a regular exerciser has been found to effectively alter stereotypes related to warmth and competence for adults with a physical disability; however, it remains unclear how sport participation can influence this trend. Therefore, this study aimed to examine warmth and competence perceptions of adults with a physical disability portrayed as elite and nonelite athletes relative to other athletic and nonathletic subgroups of adults with and without a physical disability in the context of the stereotype content model. Using survey data from able-bodied participants (N = 302), cluster analyses were applied to a behaviors from intergroup affect and stereotypes map for displaying the intersection of warmth and competence perceptions. The results demonstrated that adults with a physical disability who are described as elite athletes (i.e., Paralympians) are clustered with high warmth and high competence, similar to their able-bodied athletic counterparts (i.e., Olympians). The findings suggest that perceiving athletic and elite sport statuses for adults with a physical disability may counter the stereotypes commonly applied to this group.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.339
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.049
GPT teacher head0.323
Teacher spread0.274 · 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 teacher head, 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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

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