Natural kind and entitative beliefs in relation to prejudice toward mental disorders
Bibliographic record
Abstract
Abstract Mental health campaigns often promote biogenetic beliefs to reduce stigma, but their effectiveness may vary across disorders. Our study (N = 127) examined two components of essentialist beliefs—entitative (i.e., characterizing groupness) and natural kinds (i.e., biogenetic)—about two stigmatized mental disorders (schizophrenia, alcoholism) as well as a somatic disorder (Parkinson's disease), and their relation to prejudice. The three disorders significantly differed in natural kind beliefs (Parkinson's highest, then schizophrenia, and alcoholism lowest) and prejudice (alcoholism highest, then schizophrenia, and Parkinson's lowest), but not entitative beliefs. Entitative beliefs, however, was a stronger predictor of prejudice against schizophrenia than natural kind beliefs even after controlling for social dominance orientation and prior contact. Implications for anti‐stigma efforts and strategies are discussed.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".