Quantitative controls on the number and/or location of alcohol retail outlets: an overview of approaches in the USA and Canada
Bibliographic record
Abstract
BACKGROUND: Regulatory approaches to alcohol availability vary widely, yet policies that control the number or location of alcohol retail outlets remain under-documented. This study aimed to identify and describe these approaches across jurisdictions in the USA and Canada. METHODS: A two-stage document analysis was conducted for all 50 states, 10 Canadian provinces, and three Canadian territories (jurisdictions). Relevant legislative documents were identified and reviewed, with data extracted on policies establishing quantitative thresholds on either total outlet numbers (including outright prohibition), outlets numbers per population, distances between alcohol retail outlets, or distances between alcohol retail outlets and sensitive locations such as schools. Data collection took place between March and May 2024. RESULTS: Among 63 jurisdictions, 56 (88.9 %) used at least one approach to limiting the number and/or location of alcohol retail outlets, with 39 (61.9 %) using two or more. The most common approach (63.5 % of jurisdictions) was limiting minimum distances between outlets and specific locations, such as schools or places-of-worship. Population-based limits on outlet density were used by 44.4 % of jurisdictions, but thresholds varied substantially, (e.g. from 1.36 to 200 outlets per 100,000 population for on-sales, and 5.00 to 200 per 100,000 population for off-sales). Nearly half of all jurisdictions (47.6 %) had at least one dry county or area, while a smaller proportion (17.5 %) set minimum distances between outlets to prevent clustering. Fixed caps on the absolute number of outlets, regardless of population size, were least common (12.7 %). CONCLUSION: Quantitative controls on the number and/or location of alcohol retail outlets are widely used across the USA and Canada but vary significantly in structure and stringency. While some jurisdictions impose multiple controls, others apply none. Understanding these policy approaches provides insight into regulatory frameworks but does not indicate enforcement levels or public health impact. Further research could examine how these measures are implemented and whether different models influence alcohol availability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.031 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".