Intergroup Contact Effects via Ingroup Distancing among Majority and Minority Groups: Moderation by Social Dominance Orientation
Bibliographic record
Abstract
Five studies tested whether intergroup contact reduces negative outgroup attitudes through a process of ingroup distancing. Based on the deprovincialization hypothesis and Social Dominance Theory, we hypothesized that the indirect effect of cross-group friendship on outgroup attitudes via reduced ingroup identification is moderated by individuals' Social Dominance Orientation (SDO), and occurs only for members of high status majority groups. We tested these predictions in three different intergroup contexts, involving conflictual relations between social groups in Germany (Study 1; N = 150; longitudinal Study 2: N = 753), Northern Ireland (Study 3: N = 160; Study 4: N = 1,948), and England (Study 5; N = 594). Cross-group friendship was associated with reduced ingroup identification and the link between reduced ingroup identification and improved outgroup attitudes was moderated by SDO (the indirect effect of cross-group friendship on outgroup attitudes via reduced ingroup only occurred for individuals scoring high, but not low, in SDO). Although there was a consistent moderating effect of SDO in high-status majority groups (Studies 1-5), but not low-status minority groups (Studies 3, 4, and 5), the interaction by SDO was not reliably stronger in high- than low-status groups. Findings are discussed in terms of better understanding deprovincialization effects of contact.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".