Status conferral in intergroup social dilemmas: Behavioral antecedents and consequences of prestige and dominance.
Bibliographic record
Abstract
Bridging the literatures on social dilemmas, intergroup conflict, and social hierarchy, the authors systematically varied the intergroup context in which social dilemmas were embedded to investigate how costly contributions to public goods influence status conferral. They predicted that contribution behavior would have opposite effects on 2 forms of status-prestige and dominance-depending on its consequences for the self, in-group and out-group members. When the only way to benefit in-group members was by harming out-group members (Study 1), contributions increased prestige and decreased dominance, compared with free-riding. Adding the option of benefitting in-group members without harming out-group members (Study 2) decreased the prestige and increased the dominance of those who chose to benefit in-group members via intergroup competition. Finally, sharing resources with both in-group and out-group members decreased perceptions of both prestige and dominance, compared with sharing them with in-group members only (Study 3). Prestige and dominance differentially mediated the effects of contribution behavior on leader election, exclusion from the group, and choices of a group representative for an intergroup competition. Taken together, these findings show that the well-established relationship between contribution and status is moderated by both the intergroup context and the conceptualization of status.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".