"Supercrip" vs human interest: Examining stereotypes towards paralympians following the viewing of Canadian paralympic committee videos
Bibliographic record
Abstract
The media typically portrays Paralympians by emphasizing their superhuman qualities (i.e. a supercrip portrayal) or the characteristics of their disability (i.e. a human interest portrayal). While these portrayals may be of interest to people without physical disabilities (PD), they are perceived negatively by people with PD. No studies have examined the effect of different types of media portrayals on disability stereotypes. Therefore, the objective of this study was to compare the effect of these two portrayals on stereotype perceptions of individuals with and without PD. Participants (n=148 with PD; n=180 without PD) watched two Canadian Paralympic Committee videos, in counterbalanced order, that presented the same Paralympian using either a supercrip or human interest portrayal. After each video, participants rated Paralympians on measures of warmth and competence, two indicators of stereotypes. A 2(disability status) x 2(video) x 2(warmth and competence) mixed model ANOVA demonstrated that people without PD rated the human interest portrayal higher in warmth (M=4.12; SD=0.64) than those with PD (M=3.89; SD=0.80; p=.014), suggesting increased presence of stereotypes towards this portrayal amongst those without PD. Furthermore, regardless of group, warmth scores were significantly higher following viewing of the human interest portrayal (M=4.01; SD=.73) compared to ratings after viewing the supercrip video (M=3.87; SD=.77; p
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".