Tapping generalized essentialism to predict outgroup prejudices
Bibliographic record
Abstract
Psychological essentialism, the perception that groups possess inherent properties binding them and differentiating them from others, is theoretically relevant to predicting prejudice. Recent developments isolate two key dimensions: essentialistic entitativity (EE; groups as unitary, whole, entity-like) and essentialistic naturalness (EN; groups as fixed and immutable). We introduce a novel question: does tapping the covariance between EE and EN, rather than pitting them against each other, boost prejudice prediction? In Study 1 (re-analysis of Roets & Van Hiel, 2011b, Samples 1-3, in Belgium) and Study 2 (new Canadian data) their common/shared variance, modelled as generalized essentialism, doubles the predictive power relative to regression-based approaches with regard to racism (but not anti-gay or -schizophrenic prejudices). Theoretical implications are discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".