Does being perceived as an athlete with a physical disability affect non-verbal behaviours of abled-bodied individuals?
Bibliographic record
Abstract
Individuals with a physical disability may experience less favourable non-verbal communication when interacting with able-bodied people. However, this effect may be mitigated if the person with a disability is an athlete. Previous studies primarily use vignettes to convey disability and athlete status rather than direct in-person interactions. The purpose of this study was to compare the non-verbal communication of able-bodied individuals when they were interacting with a person with a physical disability who is either an athlete or non-athlete. Adult participants (n=56; Mage=22.2; SD=3.46) were ostensibly recruited for a marketing competition, whereby interviewing potential team members was the task. Participants interviewed a confederate with a physical disability who was portrayed as either an athlete or non-athlete through the use of a resume that participants read prior to the interview. Non-verbal behaviours (head nods, forward lean, physical distance and positive and negative facial expressions) were video recorded through one-way glass. The videos were independently coded by two research assistants who were blind to the condition. Separate one-way ANCOVA with age as a covariate were conducted for each behaviour. Participants in the non-athlete condition had a higher number of head nods – a positive non-verbal behaviour – than participants in the athlete condition. (F(1,53)=4.99, p=.03, ?p2=.066). No other significant effects emerged. Contrary to hypotheses, athlete status of the confederate with a disability did not positively impact the non-verbal behaviours of able-bodied participants' during an interaction. Participants' non-verbal behaviours may have been biased by the knowledge of being evaluated in a mock marketing competition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".