Primary and secondary generalization effects from Black and gay contact: Longitudinal evidence of between‐ and within‐person effects
Bibliographic record
Abstract
Abstract The contact hypothesis stipulates that contact between social groups can reduce intergroup prejudice, implying that contact changes people (i.e., within‐person effects). However, recent research suggests that more intergroup contact might simply be associated with less intergroup prejudice (i.e., between‐person effects). We explore primary but also secondary contact effects, whereby contact with one outgroup theoretically improves attitudes towards other uninvolved groups. White, heterosexual Americans' contact with Black and gay people was assessed at four timepoints, 3 weeks apart (T1 N = 456; 51.6% women, M age = 46.71, SD = 15.30); multilevel modelling parsed between‐ from within‐person contact effects on intergroup outcomes (attitudes, humanization, collective action intentions). We found consistent evidence of predicted primary contact effects, reflecting both within‐ and between‐subjects relations. For secondary contact, between‐subjects gay‐to‐Black associative generalization was observed: greater contact (quantity and quality) with gay people was observed among those expressing more positive Black intergroup outcomes. Within‐subjects secondary effects were primarily observed in terms of assessing contact quantity, where more contact with Black people predicted more positive gay intergroup outcomes downstream (i.e., Black‐to‐gay process generalization). Contrary to recent concerns, the current study promisingly shows that contact with a primary outgroup can change people in ways that generate positive outcomes towards primary and (some) secondary outgroups.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".