Alcohol sales changes in a Canadian province after recreational cannabis legalization
Bibliographic record
Abstract
BACKGROUND: Cannabis legalization's impacts partly depend on how it affects use of other substances like alcohol. This observational study analyzed alcohol sales in the Canadian province of Nova Scotia, where some government-owned stores sold both cannabis and alcohol. METHODS: The study compared monthly alcoholic beverage sales in Canadian dollars at Nova Scotia liquor stores during 17 months before cannabis legalization and 17 months afterward, i.e., May 2017 to February 2020. Comparative interrupted time series models of aggregate sales contrasted stores that kept selling only alcohol versus those that also began selling cannabis. RESULTS: Post-legalization alcohol sales by alcohol-only stores and cannabis-selling stores had significantly different initial responses, ongoing trends, and 17-month averages. Cannabis sellers saw initial increases of 0.55 % followed by monthly growth of 0.29 %, whereas alcohol-only stores saw initial decreases of 2.91 % followed by monthly growth of 0.06 %. Post-legalization alcohol sales consequently averaged 3.1 % above pre-legalization levels at cannabis sellers but 2.4 % below at alcohol-only stores; combined sales were 1.2 % below. Differences were larger for beers than for spirits or wines. Results were similar when considering similarly sized stores, stores' proximity to cannabis sellers, and alternative model specifications. CONCLUSIONS: Nova Scotia's alcohol sales declined slightly after cannabis legalization, while changes at alcohol-only stores differed from those at stores also selling cannabis. The changes were consistent with government-owned cannabis retailing only marginally affecting consumers' alcohol purchasing, apparently triggering some substitution and co-use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".