Nightly variation of disorder in a Canadian nightclub
Bibliographic record
Abstract
OBJECTIVE: = 258 nights). METHODS: The security staff of a large Canadian nightclub agreed to note detailed information on every intervention in which they were involved. Bouncers wrote detailed narratives of each incident of aggression and incivility that occurred in the bar. Environmental characteristics (e.g. number of admissions and alcohol sales) were collected by one of the co-authors. RESULTS: "Hot nights" were observed. The number of problem events was particularly high on Tuesday nights, which had the highest number of customers admitted and higher alcohol sales. The average alcohol sale per customer was also higher during long weekends, and alcohol sales were positively related to problem events. Finally, path analyses revealed that the presence of more bouncers was a deterrent. CONCLUSIONS: The level of disorder in a bar varies greatly over time. Contrary to what is often postulated, bars are not always high- or low-risk. The results strongly support responsible alcohol-serving policies and highlight the benefits of adequate surveillance.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".