Lay beliefs about the causes of and solutions to dehumanization and prejudice: do nonexperts recognize the role of human–animal relations?
Bibliographic record
Abstract
Abstract We investigate laypeople's beliefs about the causes of and solutions to out‐group dehumanization and prejudice. Specifically, we examine whether nonexperts recognize the role that beliefs in the human–animal divide play in the formation and reduction of intergroup biases, as observed empirically in the interspecies model of prejudice. Interestingly, despite evidence in the present study that human–animal divide beliefs predict greater dehumanization and prejudice, participants strongly rejected the human–animal divide as a probable cause of (or solution to) dehumanization or prejudice. We conclude with a meta‐analytic test of the relation between human–animal divide and prejudice (mean r = .34) in the literature, establishing the human–animal divide as an important but largely unrecognized prejudice precursor. Applied implications for the development and implementation of prejudice interventions are considered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".