Battling ingroup bias with effective intergroup leadership
Bibliographic record
Abstract
Intergroup conflict and bias often occur between subgroups nested within a superordinate group. In these situations, the leader of the superordinate group plays a key role, as an intergroup leader, in reducing conflict. To be effective, an intergroup leader should avoid (1) threatening the subgroups' distinctive identities, and (2) being viewed by one or both groups as 'one of them' rather than 'one of us'. Intergroup leadership theory (Acad Manag Rev, 37, 2012a, 232) posits intergroup leaders can improve subgroup relations by promoting an intergroup relational identity. Two studies (Ns = 178 and 223) tested whether an out-subgroup or in-subgroup leader could improve intergroup attitudes, even among strong subgroup identifiers, by promoting either an intergroup relational identity or a collective identity. We hypothesized an interaction of these variables demonstrating the effectiveness of an intergroup relational identity message for an out-subgroup leader in lessening ingroup bias, especially among strong subgroup identification. Our results, and a meta-analytic summary across both studies (N = 401), supported our hypothesis and intergroup leadership theory, demonstrating an intergroup relational identity is an effective strategy for improving intergroup relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".