Understanding risk‐based licensing schemes for alcohol outlets: A key informant perspective
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Risk-based alcohol licensing (RBL) has been introduced in several jurisdictions in Australia, New Zealand and Canada with the intention of reducing harm in and around alcohol outlets. RBL involves tailoring licence fees or regulatory agency monitoring levels according to risk criteria such as trading hours, venue size and compliance history. The aim of this study was to document key informant perspectives including their perceptions of the purpose of RBL, how it works and its active ingredients. DESIGN AND METHODS: We conducted semi-structured in-depth interviews with 28 key informants, including four government policy makers, four liquor licensing representatives, four local council members, eight police officers, six licensees, one academic and one community advocate from Victoria, Queensland, the Australian Capital Territory and Ontario, Canada. We analysed the transcripts using a thematic approach. RESULTS: Informants varied in their opinions about whether RBL achieved its objective of reducing alcohol-related harm. They identified difficulties in enforcing the compliance history component of the scheme due to loopholes in legislation as a major shortfall, and the need to apply RBL to packaged liquor (off-licence) outlets. They also discussed the need to consider outlet density associated with the location of a venue when assessing venue risk. DISCUSSION AND CONCLUSIONS: RBL schemes vary by jurisdiction and emphasise different components. In general, informants surmised that RBL as implemented has probably had little or no preventive effect but suggested that it may be effective with greater monitoring and penalties large enough to deter bad practice.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".