Predisposed to prejudice but responsive to intergroup contact? Testing the unique benefits of intergroup contact across different types of individual differences
Bibliographic record
Abstract
Recent research demonstrates that intergroup contact effectively reduces prejudice even among prejudice-prone persons. But some assert that evidence regarding the benefits of contact among prejudice-prone individuals is “mixed,” particularly for those higher in social dominance orientation (SDO), one of the field’s most important individual differences. Problematically, person variables are typically considered in isolation despite being intercorrelated, leaving the question of which unique psychological aspects of prejudice proneness (e.g., authoritarianism, antiegalitarianism, cognitive style) are responsive to intergroup contact unresolved. To address this shortcoming, in a large sample of White Americans ( N = 465) we simultaneously examined the contact–attitude association at varying levels of ideological (SDO, right-wing authoritarianism), cognitive style (need for closure), and identity-based (group identification) indicators of prejudice proneness. Examining a broad range of intergroup criterion measures (e.g., racism, support for racial profiling) we reveal that greater contact quality is associated with lower levels of intergroup hostility for those both lower and higher on a variety of indicators of prejudice proneness, simultaneously considered.
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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 | 0.002 |
| 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".