Who Cries Wolf, and When? Manipulation of Perceived Threats to Preserve Rank in Cooperative Groups
Bibliographic record
Abstract
People perform greater within-group cooperation when their groups face external threats, such as hostile outgroups or natural disasters. Researchers and social commentators suggest that high-ranking group members manipulate this "threat-dependent" cooperation by exaggerating threats in order to promote cooperation and suppress competition for their position. However, little systematic research tests this claim or possible situational moderators. In three studies, we use a cooperative group game to show that participants pay to increase others' perceptions of group threats, and spend more on manipulation when holding privileged positions. This manipulation cost-effectively elicits cooperation and sustains privilege, and is fostered by competition over position, not only position per se. Less cooperative people do more manipulation than more cooperative people do. Furthermore, these effects generalize to broader definitions of privilege. Conceptually, these results offer new insights into an understudied dimension of group behavior. Methodologically, the research extends cooperative group games to allow for analyzing more complex group dynamics.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".