Apparent motives for aggression in the social context of the bar.
Bibliographic record
Abstract
OBJECTIVE: Little systematic research has focused on motivations for aggression and most of the existing research is qualitative and atheoretical. This study increases existing knowledge by using the theory of coercive actions to quantify the apparent motives of individuals involved in barroom aggression. Objectives were to examine: gender differences in the use of compliance, grievance, social identity, and excitement motives; how motives change during an aggressive encounter; and the relationship of motives to aggression severity. METHOD: evaluation. Trained coders rated each type of motive for the 1,507 bar patrons who engaged in aggressive acts. RESULTS: Women were more likely to be motivated by compliance and grievance, many in relation to unwanted sexual overtures from men; whereas men were more likely to be motivated by social identity concerns and excitement. Aggressive acts that escalated tended to be motivated by identity or grievance, with identity motivation especially associated with more severe aggression. CONCLUSIONS: A key factor in preventing serious aggression is to develop approaches that focus on addressing identity concerns in the escalation of aggression and defusing incidents involving grievance and identity motives before they escalate. In bars, this might include training staff to recognize and defuse identity motives and eliminating grievance-provoking situations such as crowd bottlenecks and poorly managed queues. Preventive interventions generally need to more directly address the role of identity motives, especially among men.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".