Intergroup Leadership: The Challenge of Successfully Leading Fractured Groups and Societies
Bibliographic record
Abstract
Leadership often involves providing leadership to a group that is structured into separate subgroups that have distinctive self-contained identities that are cherished by their members. In this respect, leadership can be characterized as intergroup leadership. The challenge of intergroup leadership is to forge a superordinate identity that does not erase or blur the subgroups’ identity boundaries and create a threat to the subgroups’ social identity distinctiveness, as such identity threat can provoke intersubgroup conflict that fragments the superordinate group. According to intergroup-leadership theory, successful intergroup leaders need to construct, promote, and exemplify an intergroup relational identity that preserves subgroups’ distinctiveness and celebrates that distinctiveness and intersubgroup cooperation as fundamental aspects of subgroup and superordinate-group identity. In this article, we describe intergroup-leadership theory (which applies to groups of all sizes and complexions), summarize empirical support for its main tenets, and outline extensions and future directions.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".