The relationship between disability identity and use of person-first and identity-first language.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Debate continues over disability language preferences, with some individuals favoring person-first language and others preferring identity-first language. Not all people with disabilities identify as such, and language use may be influenced by an individual's disability identity. This study examined the relationship between disability identity and language use for self-identification, referring to others, and perceived appropriateness across different contexts. RESEARCH METHOD/DESIGN: = 39.9 years; 72.8% female, 17.5% nonbinary; 87.2% from Australia, 6.1% from the United States, 3.6% from the United Kingdom) completed a survey on demographic and health-related factors, disability identity, disability language use, and perceived appropriateness of disability language use across different contexts. Multiple linear and multinomial logistic regression models explored relationships among these variables. RESULTS: Participants with a stronger disability identity were more likely to use identity-first language and perceived it as more appropriate across contexts, while those with a lower disability identity preferred person-first language. Several demographic and health-related factors significantly predicted stronger disability identity, including younger age, nonbinary gender, residence in "other" countries (e.g., New Zealand, Canada, and European countries excluding the United Kingdom), neurodevelopmental, hearing, digestive, or pain conditions, congenital conditions, and having visible or both visible and invisible disabilities. CONCLUSIONS/IMPLICATIONS: These findings contribute to a deeper understanding of disability identity and language preferences. Future research should explore how personal and environmental contexts influence language use among people with disabilities, as addressing individuals with dignity and respect is essential in advancing toward a more inclusive society. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.012 |
| 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) | 0.005 | 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".