COST‐BENEFIT ANALYSES FOR YOUR GROUP AND YOURSELF: THE RATIONALITY OF DECISION‐MAKING IN CONFLICT
Bibliographic record
Abstract
Two studies in the context of English‐French relations in Québec suggest that individuals who strongly identify with a group derive the individual‐level costs and benefits that drive expectancy‐value processes (rational decision‐making) from group‐level costs and benefits. In Study 1, high identifiers linked group‐ and individual‐level outcomes of conflict choices whereas low identifiers did not. Group‐level expectancy‐value processes, in Study 2, mediated the relationship between social identity and perceptions that collective action benefits the individual actor and between social identity and intentions to act. These findings suggest the rational underpinnings of identity‐driven political behavior, a relationship sometimes obscured in intergroup theory that focuses on cognitive processes of self‐stereotyping. But the results also challenge the view that individuals' cost‐benefit analyses are independent of identity processes. The findings suggest the importance of modeling the relationship of group and individual levels of expectancy‐value processes as both hierarchical and contingent on social identity processes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".