Exploring stereotypes of athletes with a disability: Multiple mediation analyses using the stereotype content model
Bibliographic record
Abstract
Both International and National Paralympic Committees have recently begun to focus on quantifying the potential social legacy fostered by societal exposure to Paralympic competition. A key aspect of this desired social legacy includes changing negative stereotypes often held by able-bodied adults towards individuals with disabilities. Using tenets of the Stereotype Content Model (SCM), the present study aimed to assess the stereotypes, emotions, and behaviours associated with Paralympic sport and recreational sport participants depicted as able-bodied or having a physical disability (i.e., Olympians, Paralympians, and recreational athletes with or without a physical disability). Multivariate analyses of survey results from 302 able-bodied adults revealed admiration was the strongest SCM emotion elicited for all sport groups, regardless of ability. While Olympians were rated with significantly higher admiration compared to the other groups (M=4.4/5, SD=0.8, ps0.05). Additionally, active and passive facilitation behaviours were most prominently reported; however, mediation analyses highlighted feelings of admiration significantly contributed to these behaviours moreso than any other SCM emotions. Considering feelings of admiration and positive facilitation behaviours were rated significantly higher than negative SCM feelings and behaviours (e.g., contempt, active harm, etc.), these findings suggest elite and recreational sport participation may mitigate negative societal stereotypes commonly associated with physical disability. The implications of such results address feasibility concerns of Paralympic Committees related to achieving a more positive social legacy stemming from viewing Paralympic competition.Acknowledgments: The Social Sciences and Humanities Research Council of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".