Divided groups need leadership: A study of the effectiveness of collective identity, dual identity, and intergroup relational identity rhetoric
Bibliographic record
Abstract
Abstract Reducing intergroup conflict is a significant leadership challenge. Leaders can alleviate conflict by promoting a collective, dual, or intergroup relational identity, but they should avoid provoking subgroup identity distinctiveness threat. Drawing on intergroup leadership theory, we conducted an experiment ( N = 184) examining evaluations of a leader who promoted a dual, collective, or intergroup relational identity under low or high subgroup identity distinctiveness threat. We hypothesized that identity distinctiveness threat would improve evaluations of a leader promoting an intergroup relational identity, and worsen evaluations of a leader promoting a collective identity. Although a leader promoting a dual identity is typically preferred to one promoting a collective identity, we expected a leader promoting dual identity to receive worse evaluations than a leader promoting an intergroup relational identity. These hypotheses were supported, providing additional support for intergroup leadership theory and demonstrating the utility of employing intergroup relational identity rhetoric.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".