Bad nights or bad bars? Multi‐level analysis of environmental predictors of aggression in late‐night large‐capacity bars and clubs
Bibliographic record
Abstract
AIMS: To clarify environmental predictors of bar-room aggression by differentiating relationships due to nightly variations versus across bar variations, frequency versus severity of aggression and patron versus staff aggression. DESIGN, SETTING AND PARTICIPANTS: Male-female pairs of researcher-observers conducted 1334 observations in 118 large capacity (> 300) bars and clubs in Toronto, Canada. MEASUREMENTS: Observers independently rated aspects of the environment (e.g. crowding) at every visit and wrote detailed narratives of each incident of aggression that occurred. Measures of severity of aggression for the visit were calculated by aggregating ratings for each person in aggressive incidents. FINDINGS: Although bivariate analyses confirmed the significance of most environmental predictors of aggression identified in previous research, multivariate analyses identified the following key visit-level predictors (controlling for bar-level relationships): rowdiness/permissive environment and people hanging around after closing predicted both frequency and severity of aggression; sexual activity, contact and competition and people with two or more drinks at closing predicted frequency but not severity of aggression; lack of staff monitoring predicted more severe patron aggression, while having more and better coordinated staff predicted more severe staff aggression. Intoxication of patrons was significantly associated with more frequent and severe patron aggression at the bar level (but not at the visit level) in the multivariate analyses and negatively associated with severity of staff aggression at the visit level. CONCLUSIONS: The results demonstrate clearly the importance of the immediate environment (not just the type of bar or characteristics of usual patrons) and the importance of specific environmental factors, including staff behaviour, in predicting both frequency and severity of 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".