‘Hotspots’ for aggression in licensed drinking venues
Bibliographic record
Abstract
INTRODUCTION AND AIMS: In order to better understand the social context of barroom aggression, the aim was to identify common locations ('hotspots') for aggression in bars and examine the association of hotspots with aggression severity and environmental characteristics. DESIGN AND METHODS: Aggression hotspots were identified using narrative descriptions and data recorded on premises' floor plans for 1057 incidents of aggression collected in the Safer Bars evaluation. Hierarchical Linear Modelling was used to identify bar-level and night-level characteristics associated with each hotspot. RESULTS: The most common location for aggression was the dance floor (20.0% of incidents) or near the dance floor (11.5%), followed by near the serving bar (15.7%), at tables (13.1%), aisles, hallways and other areas of movement (6.2%), entrance (4.5%) and the pool playing area (4.1%). Hotspots were predicted mainly by bar-level characteristics, with dance floor aggression associated with crowded bars, a high proportion of female and young patrons, lots of sexual activity, a large number of patrons and staff, security staff present, better monitoring and coordination by staff, and people hanging around at closing. Incidents at tables and pool tables tended to occur in bars with the opposite characteristics. Nightly variations in patron intoxication and rowdiness were associated with aggression at tables while variations in crowding and sexual activity were associated with aggression in areas of movement. Incidents outside tended to be more severe. DISCUSSION AND CONCLUSIONS: Each aggression location and their associated environments have somewhat different implications for staff training, premises design, policy and prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".