Rejecting People-First Language: Predictors and Causes of the Use of Noun-Based Mental Disorder Labels
Bibliographic record
Abstract
Psychiatric noun labels such as ‘schizophrenic’ carry with them a host of negative beliefs, attitudes and assumptions, but no research to date has demonstrated a causal link between negative portrayals of a person with mental illness and the tendency to describe such an individual with a noun. The current research investigated (1) whether depicted violence increases the use of noun labels to describe an individual with a psychological disorder, and (2) whether dehumanization processes and/or perceived threat of the target person mediate this relationship. University undergraduates (N = 313) read two mock newspaper stories in counterbalanced order: one depicting a man with schizophrenia committing a nonviolent crime and one depicting a man with schizophrenia committing a highly violent crime. Participants completed measures of dehumanization and perceived threat in relation to the target individual in each scenario. Respondents were then tasked with selecting seven headlines for each of the two news stories, in each case choosing between headlines employing either a noun label (e.g., Schizophrenic Snaps) or a possessive label (e.g., Person with Schizophrenia Snaps). As predicted, violent depictions of a person with schizophrenia increased the use of noun label headlines, and dehumanization processes were found to mediate this relationship. Several implications of these findings are discussed. Discipline: Psychology Faculty Mentor: Dr. Andrew Howell
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".