Exploring Associations between Cannabis Prices, Stores, and Usage after Recreational Legalization
Bibliographic record
Abstract
INTRODUCTION: Canada legalized recreational cannabis in October 2018, but commercial retailing took time to develop. This study first explored how self-reported cannabis use prevalence, daily use, product type use, and age of initial use changed during 2019-2023. It then analyzed whether the changes were associated with rising store numbers or falling prices. METHODS: Data on store counts, retail pricing, and cannabis use came from government reports covering 10 provinces over 5 years. Panel data linear regressions analyzed 50 province-year aggregated observations. RESULTS: There were no significant changes in prevalence among males and people aged 16-24 or in the proportion using cannabis daily. Prevalence among females and people aged 25+ increased; those levels showed negative associations with prices but not stores. Dried cannabis use decreased, while edibles use increased; those also showed associations with prices but not stores. Mean initial age of use increased; it was negatively associated with prices and positively with stores. CONCLUSION: Canada's large cannabis retail expansion was accompanied by relatively modest usage changes, most of which showed associations with falling prices but not rising store counts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".