They fight because we let them! Applying a situational crime prevention model to barroom violence
Bibliographic record
Abstract
This paper draws on situational crime prevention and social interactionist theories to develop a theoretical model for preventing aggression and injury in licensed drinking establishments. The model includes six components: (i) the presence of willing participants; (ii) the role of guardians, handlers and place managers; (iii) environmental deterrents of aggression; (iv) environmental precipitators of aggression; (v) theories regarding the effects of alcohol that contribute to increased risk of aggression; and (vi) social interaction theory relevant to the process of aggressive incidents in drinking establishments. The model provides a basis for developing a range of strategies to reduce barroom aggression that includes but is not restricted to addressing the role of alcohol. The paper discusses how these strategies can be developed and implemented through interventions, programs, policies or regulations at the level of the drinking establishment, the community level or the state/national level.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".