Does Language Type Affect Perceptions of Disability Images? An Experimental Study
Bibliographic record
Abstract
The present experimental study examined the impact of language type on perception of disability images with text captions. 204 disability naïve undergraduate students viewed disability images containing one of six disability language captions: disability-first, defiant self-naming, impairment, negative, person-first, and apologetic naming. Participants completed measures of identification, emotion, willingness to help, willingness to include, and perceptions of capabilities and rights. Person-first and apologetic naming did not result in more positive perceptions of disability. Rather, defiant self-naming evoked the most positive emotions and identification, and greater perceived capabilities and willingness to include whereas negative language evoked the most negative perceptions of images. Results suggest that the elimination of negative language and the use of empowering defiant self-naming by people with disabilities, rather than a focus on using person-first and apologetic naming, may be more effective in reducing negative disability stereotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".