How Can Intergroup Interaction Be Bad If Intergroup Contact Is Good? Exploring and Reconciling an Apparent Paradox in the Science of Intergroup Relations
Bibliographic record
Abstract
The outcomes of social interactions among members of different groups (e.g., racial groups, political groups, sexual orientation groups) have long been of interest to psychologists. Two related literatures on the topic have emerged-the intergroup interaction literature and the intergroup contact literature-in which divergent conclusions have been reported. Intergroup interaction is typically found to have negative effects tied to intergroup bias, producing heightened stress, intergroup anxiety, or outgroup avoidance, whereas intergroup contact is typically found to have positive effects tied to intergroup bias, predicting lower intergroup anxiety and lower prejudice. We examine these paradoxical findings, proposing that researchers contributing to the two literatures are examining different levels of the same phenomenon and that methodological differences can account for the divide between the literatures. Further, we introduce a mathematical model by which the findings of the two literatures can be reconciled. We believe that adopting this model will streamline thinking in the field and will generate integrative new research in which investigators examine how a person's experiences with diversity unfold.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".