Bibliographic record
Abstract
Background: Evolutionary theory suggests prejudice may be a result of the evolution of human sociality. In this study, we investigate this claim by integrating theoretical insights of evolutionary theory with the well-established social psychological research on prejudice centering on Right Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) as the main predictors of prejudice. Method: First, we developed two different signaling scales, probing respondents’ propensity to signal group commitment in a genuine or deceptive way. We administered a questionnaire consisting of the two signaling measures, RWA, SDO and prejudice measures to 1380 students. Analysis of the data was done using structural equation modeling. Results: Our results indicate that genuine signaling of one’s commitment to the in-group is positively associated with RWA, and that deceptively signaling one’s commitment to the in-group is positively associated with SDO. Both RWA and SDO are positively related to prejudice. Conclusion: Our study is the first to empirically reveal the pro-social roots of prejudice using classical measurement instruments. The findings give rise to a new array of research questions.
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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 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| 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".