Social Cure Processes Help Lower Intergroup Anxiety Among Neighborhood Residents
Bibliographic record
Abstract
Research in the social cure tradition shows that groups can reduce members' stress by providing support to cope with challenges, but it has yet to consider how this applies to the anxiety occasioned by outgroups. Research on intergroup contact has extensively examined how reducing intergroup anxiety improves attitudes towards outgroups, but it has yet to examine the role of intragroup support processes in facilitating this. The present article takes the case of residential contact, in which the impact of diversification upon neighborhood cohesion is hotly debated, but the role of neighborhood identification and social support from neighbors in facilitating residential mixing has been largely ignored. Our surveys of two geographically bounded communities in England (n = 310; n = 94) and one in Northern Ireland (n = 206) show that neighborhood identification predicts both well‐being and more positive feelings towards outgroups, with both effects occurring via increased intragroup support. In studies 2 and 3, we show that this positive effect on feelings towards the outgroup occurs independently of that of intergroup contact and is further explained by the effect of neighborhood support in reducing intergroup anxiety. This suggests that social cure processes can improve intergroup attitudes by supporting group members to deal with the stress of intergroup interactions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".