Grumbling, voting, demonstrating, and rioting: ''Rationality'' and decision-making in intergroup conflict
Bibliographic record
Abstract
An individual faced with intergroup conflict chooses A from a vast array of possible actions, ranging from grumbling among ingroup friends to voting and demonstrating to rioting and revolution. The present paper conceptualises these intergroup choices as rationally shaped by perceptions of the benefits and costs associated with the action (expectancy-value processes). However, in presenting a model of agentic normative influence, it is argued that in intergroup contexts group-level costs and benefits play a critical role in individuals' decision-making. In the context of English-French conflict in Quebec, in Canada, four studies provide evidence that group-level costs and benef influence individuals' decision-making in intergro conflict; that the individual level of analysis need mediate the group level of analysis; that group-level co and benefits mediate the relationship between soc identity and intentions to engage in collective action; a that perceptions of outgroup and ingroup norms for inte group behaviours are relatively invariant and predictal related to perceptions of the group- and individual-le, benefits and costs associated with individualistic vers collective actions. By modelling the relationship betwe group norms and group-level costs and benefits, soc psychologists may begin to address the processes th underlie identity-behaviour relationships in collecti action and intergroup conflict.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".