Contextual effect of positive intergroup contact on outgroup prejudice
Bibliographic record
Abstract
We assessed evidence for a contextual effect of positive intergroup contact, whereby the effect of intergroup contact between social contexts (the between-level effect) on outgroup prejudice is greater than the effect of individual-level contact within contexts (the within-level effect). Across seven large-scale surveys (five cross-sectional and two longitudinal), using multilevel analyses, we found a reliable contextual effect. This effect was found in multiple countries, operationalizing context at multiple levels (regions, districts, and neighborhoods), and with and without controlling for a range of demographic and context variables. In four studies (three cross-sectional and one longitudinal) we showed that the association between context-level contact and prejudice was largely mediated by more tolerant norms. In social contexts where positive contact with outgroups was more commonplace, norms supported such positive interactions between members of different groups. Thus, positive contact reduces prejudice on a macrolevel, whereby people are influenced by the behavior of others in their social context, not merely on a microscale, via individuals' direct experience of positive contact with outgroup members. These findings reinforce the view that contact has a significant role to play in prejudice reduction, and has great policy potential as a means to improve intergroup relations, because it can simultaneously impact large numbers of people.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".