Exploring stereotypes of athletes with a disability: A behaviours from intergroup affect and stereotypes (bias) map comparison
Bibliographic record
Abstract
According to the Stereotype Content Model, individuals with a physical disability are often perceived as a social group with high warmth (e.g., friendliness) and low competence (e.g., intelligence), which subsequently tends to elicit negative feelings and behaviours. Associating this stigmatized group with physically active behaviours has proven to be a promising strategy towards modifying perceptions of warmth and competence. However, it remains unknown how associating sport with adults with a physical disability can predict this trend. Survey data from able-bodied participants (N=181) were used via cluster analyses to create a BIAS map, comparing the warmth and competence rankings of adults with a physical disability and able-bodied adults when depicted as an elite or recreational athlete (i.e., Paralympians, Olympians, recreational athletes with a physical disability, recreational able-bodied athletes) and non-athletes. All sport groups were clustered in higher competence BIAS map regions compared to non-athletes with a physical disability. Results also highlight that athletes with a physical disability are similarly clustered with their able-bodied counterparts. Moreover, an additive effect was observed regarding warmth and competence as the level of sport association became more elite. Paralympians and Olympians clustered in high warmth-high competence, while recreational able-bodied and athletes with a physical disability clustered in medium warmth-medium competence regions. These findings suggest that perceiving more active and elite sport statuses for individuals with a physical disability may mitigate the stereotypes commonly applied to this group. Future work will focus on how these modifications can influence subsequent feelings and behaviours towards adults with physical disabilities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".