Legal cannabis market shares during Canada's first year of recreational legalisation
Bibliographic record
Abstract
BACKGROUND: This study estimated legal products' share of Canada's total cannabis consumption during the first year of recreational legalisation, October 2018 to September 2019. METHODS: Government data was used to estimate monthly recreational sales in dollars per capita, grams per user, and percentage share of kilograms or litres consumed. As explanatory factors, the analysis considered provincial differences in retail pricing (percentage mark-ups) and store density (stores per million users), as well as national monthly production of dry cannabis (kilograms) and cannabis oil (litres) finished products. RESULTS: Legal recreational products' share of Canada's overall cannabis consumption began at 7.8% in October 2018 and grew to 23.7% by September 2019, with an average of 14.5% over the first 12 months. Sales growth was delayed by shortages of both dry cannabis products and licensed stores, but not cannabis oils. Across the 10 provinces, legal recreational shares in September 2019 varied from 13% to 70%; differences in store densities and retail prices partly explained the variation. Prince Edward Island's large 70% share seemed due to it having minimal product shortages, high store densities, and low prices. CONCLUSIONS: Legal recreational products captured market share to the extent they were available, accessible, and low-priced. Problems with those factors slowed the initial expansion of legal product sales but also suggested ways to gradually increase their market share.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".