Physical Disability Affects Women’s but Not Men’s Perception of Opposite-Sex Attractiveness
Bibliographic record
Abstract
Physical appearance influences our perceptions, judgments, and decision making about others. While the current literature with regard to the perceptions and judgments of nondisabled people's attractiveness is robust, the research investigating the perceived physical attractiveness and judgments of physically disabled individuals is scarce. Therefore, in the current study, we investigated whether people with physical disabilities are perceived by the opposite sex as more or less attractive relative to nondisabled individuals. Our results, based on over 675 participants, showed a positive effect for women's attractiveness ratings of men with physical disabilities, but not men's attractiveness ratings of physically disabled women. Moreover, social desirability bias was positively associated with attractiveness ratings of physically disabled individuals, meaning those with higher tendency to be viewed favorably by others rated physically disabled individuals more attractive. Finally, our results revealed that attractiveness ratings of individuals with physical disabilities are positively associated with extroversion and empathy in both men and women, and positively with agreeableness and negatively with neuroticism in women. In conclusion, our study showed women rate men with physical disabilities as higher on attractiveness than nondisabled men, which is also influenced by their social desirability bias.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".