Behavioural indicators of motives for barroom aggression: Implications for preventing bar violence
Bibliographic record
Abstract
INTRODUCTION AND AIMS: To develop new strategies for preventing violence in high-risk licensed premises, we identify behavioural indicators of apparent motives for aggression in these settings and outline the implications of different motivations for prevention. DESIGN AND METHODS: The four types of motives for aggressive or coercive acts defined by the theory of coercive actions framed the research: gaining compliance, expressing grievances/restoring justice, attaining a favourable social identity and pursuing fun/excitement. Incidents of aggression from the Safer Bars evaluation research were analysed to identify behavioural indicators of each motivation. RESULTS: Compliance-motivated aggression typically takes the form of unwanted social overtures, third party intervention to stop conflicts or staff rule enforcement. Prevention strategies include keeping the aggressor's focus on compliance to avoid provoking grievance and identity motives that are likely to escalate aggression. Grievance motives are typically elicited by perceived wrongdoing and therefore prevention should focus on eliminating sources of grievances and adopting policies/practices to resolve grievances peacefully. Social identity motives are endemic to many drinking establishments especially among male patrons and staff. Prevention involves reducing identity cues in the environment, hiring staff who do not have identity concerns, and training staff to avoid provoking identity concerns. Aggression motivated by fun/excitement often involves low-level aggression where escalation can be prevented by avoiding grievances and attacks on identity. DISCUSSION AND CONCLUSIONS: Knowledge of behavioural indicators of motives can be used to enhance staff hiring and training practices, reduce environmental triggers for aggression, and develop policies to reduce motivation for aggression.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".