Transitions to legal cannabis markets: Legal market capture of cannabis expenditures in Canada following federal cannabis legalization
Bibliographic record
Abstract
BACKGROUND: Canada legalized 'recreational' or 'non-medical' cannabis in 2018 with a primary objective of displacing illicit cannabis and transitioning consumers to a 'quality controlled' legal retail market. To date, there is limited research on legal market capture in jurisdictions with non-medical cannabis markets. METHODS: The current analysis used 'demand-side' methods to estimate the size of the Canadian cannabis market using data from two sources. First, data from the Canadian Community Health Survey were used to estimate the number of Canadians who use cannabis. Second, data on cannabis expenditures from legal versus illegal sources were analyzed from 5656 past 12-month consumers aged 16-100 who completed national surveys conducted in 2022 as part of the International Cannabis Policy Study. RESULTS: Total estimated expenditures from legal sources were within two percentage points of the 'actual' retail sales data from Government of Canada's tracking system. In the 12-month period ending in September 2022, total cannabis expenditures in Canada were estimated at $6.72 billion dollars, including $5.23 billion from legal sources and $1.49 billion from illegal sources for an estimated legal market capture of 78 %. In 2022, dried flower accounted for 55 % of total legal expenditures and an additional 2 % was spent on plants and seeds. Concentrates accounted for 12 % of legal expenditures, followed by oral liquids (11 %), vaping liquids (10 %), and edibles (8 %, excluding drinks). CONCLUSIONS: The findings provide evidence of substantial transition in expenditures from the illegal to the legal market in the five years since legalization of non-medical cannabis in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".