Commentary on Kypri <i>et al</i>. (2011): Fighting the good fight against alcohol‐related violence: one bar or one hour at a time?
Bibliographic record
Abstract
Some years ago I had the privilege of joining the plainclothes Perth Liquor Squad on a late-night tour of violent bars. I am sure I looked very much out of place, but two managers mistook me for a member of the force. Incredibly, one asked me to break up a fight—I referred this request on. A second asked us what we would like to drink and was clearly stunned when we all asked for lemonades. I later learned that the venue was reputed to have a special bar reserved for the Squad, where they were served food and alcohol. For this kind of reason—and a host of others such as poor training, inadequate resources, poorly worded legislation and a heavy drinking culture [1]—I suggest that law enforcement approaches alone are inadequate to address the problem of bar violence. We clearly need effective universal strategies such as restricted trading hours and outlet densities. I congratulate Dr Kypri and his colleagues on their fine contribution [2] to this challenging area. With their study, three others they identify and a recent literature review [3], we now have at least 53 studies on the impact of pub trading hours on health and safety outcomes. Of these, 18 meet basic criteria of having both baseline and control data, 15 of which indicate support for the availability hypothesis, i.e. restricted hours reduce alcohol-related harm or increased hours result in greater harm. The New South Wales Bureau of Crime Statistics and Research (BOCSAR)—co-sponsors and co-authors of the report—is also to be commended for a decade-long tradition of research focusing on violence in and around licensed premises (e.g [4–7]). This research continues to influence policy in a positive way. Arguably, intense public debate generated by their research contributed to the 2003 Alcohol Summit in the parliament of New South Wales—an extraordinary event at which all sitting MPs in Australia's oldest parliament debated alcohol policy with community representatives for a week. Numerous resolutions were passed, many focusing on the problem of late-night violence around pubs [8]. The tradition continued with the publication of the names of 100 pubs with the worst record for violence which, in turn, clearly influenced the Top 48 legislation introduced by the New South Wales Premier in October 2008 [9]. This legislation was designed to reduce alcohol-related ‘assaults, glassings, intoxication and disturbance on nearby areas’ and included a range of restrictions applicable between 2 a.m. and 5 a.m., e.g. no new patrons admitted, no glass containers, no drinks containing more than 50% spirits and no takeaway drinks containing more than 5% alcohol by volume [9]. This targeted approach on the most high-risk venues is consistent with research indicating that a small minority of pubs tend to contribute the great majority of violent incidents associated with licensed premises in entertainment districts (e.g. [5]). In my experience it is rare that liquor regulation and law enforcement are informed adequately by local data. Human and computer resources are often so limited in police and licensing authorities that decisions are made without consideration of violent incidents, which are often kept on a separate database [11]. Targeted approaches to liquor law enforcement certainly have potential value (e.g. [10]) but, on the available evidence, if I were a citizen concerned about late-night violence in my neighbourhood I would also be demanding an across-the-board restriction on late-night trading. Perhaps one of the most remarkable things about this whole literature is that adding or subtracting just 1 or 2 hours of trading after midnight can make such a substantial difference to rates of violence. Kypri et al. [2] studied a restriction from 5 a.m. to 3 a.m. and estimated a 37% reduction in late-night violence. In a Perth study, bars permitted to trade just 1 or 2 hours extra after midnight were found to double the rate of late-night violent incidents reported to the police [12]. Since the days of the notorious ‘6 o'clock swill’, when the bars in New South Wales opened only for 1 hour till 6 p.m. accompanied by scenes of much mayhem, industry advocates have claimed that restricted hours would speed up drinking and make the problem worse [13]. This strange argument appears to have been so successful that the following 50 years have seen 10 hours of extra trading added incrementally for bars in that Australian state. The Kypri et al. [2] study suggests the need to call ‘Time!’ on this trend. Until we have droves of well-trained, moderate drinking and uncorruptable police supported by modern information systems publicly reporting violence pub by pub, we clearly need restricted late-night trading hours—the alleged experience of the UK relaxed liquor laws notwithstanding [3]. None.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.005 |
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; both teacher heads agree on what is shown here.
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".