The effects of system threat on intergroup interaction
Bibliographic record
Abstract
Individuals defend and rationalize social systems in order to maintain the belief that the world in which they live is fair and good. This justification often involves seeing intergroup inequality as legitimate and holding negative attitudes toward lower status groups. Although research on system justification is plentiful, the effects of perceived threat to the system on intergroup interaction behavior and dynamics have remained unexamined. With ethnic diversity increasing in North America, it is imperative that we understand the factors that promote more positive (and negative) intergroup interactions. Across three studies I examined individuals’ reactions to system threatening information versus low threat in the context of an intragroup or intergroup interaction. In general, priming dominant group members with system threat (versus low threat) led to less negative intergroup interaction behavior. Specifically, being primed with system unfairness led dominant group members in Study 1 to express more positive other-directed remarks during a written exchange with an ostensible outgroup member. Study 2, conducted with a different minority group than Study 1, found that dominant group members feel more guilt when interacting with minority group members versus members of their own group in the face of system threat. Finally, a face-to-face intergroup interaction study replicated the positive behavioral effects of salient system threat found in Study 1, this time manifest in increased nonverbal friendliness and self-disclosure for both pair members. These findings suggest that system threat instantiated in an interaction setting leads dominant group members toward exhibiting more positive behavior to minority group members that benefits both parties involved, rather than toward derogation. Implications for social change initiatives are discussed.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".