Furthering the person-first versus identity-first language debate
Bibliographic record
Abstract
The use of person-first language (i.e., the person with a disability) versus identity-first language (i.e., the disabled person) is a source of ongoing debate. Proponents of person-first language argue for its use, so as not to objectify or stereotype a person by their illness or disability. Conversely, advocates of identity-first language state that it affirms pride in the person’s disability. Overall, however, there is a growing use of identity-first language. Both proponents of person-first and identity-first language are aligned in their quest to maximise respect and inclusivity of people with disabilities and health conditions. Limited research examining the language preferences of those with disabilities and/or medical issues has been mixed. The majority of the research has focused on autism, multiple sclerosis and deaf/blind populations. In some cases, studies have methodological issues, and researchers have concentrated on the perspectives of students, employees and counsellors. Factors that may influence preferences, such as disability type, severity, acceptance and identity, have not been adequately examined in the research. Future research is required to gain an evidence-based understanding of language preferences that can improve social inclusion for people with varying disabilities and health conditions.
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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 | 0.082 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.094 |
| Scholarly communication | 0.017 | 0.047 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".