Legal sourcing of ten cannabis products in the Canadian cannabis market, 2019–2021: a repeat cross-sectional study
Bibliographic record
Abstract
BACKGROUND: One of the objectives of cannabis legalization in Canada is to transition consumers from the illegal to the legal market. Little is known about how legal sourcing varies across different cannabis product types, provinces, and frequency of cannabis use. METHODS: Data were analyzed from Canadian respondents in the International Cannabis Policy Study, a repeat cross-sectional survey conducted annually from 2019 to 2021. Respondents were 15,311 past 12-month cannabis consumers of legal age to purchase cannabis. Weighted logistic regression models estimated the association between legal sourcing ("all"/ "some"/ "none") of ten cannabis product types, province, and frequency of cannabis use over time. RESULTS: The percentage of consumers who sourced "all" their cannabis products from legal sources in the past 12 months varied by product type, ranging from 49% of solid concentrate consumers to 82% of cannabis drink consumers in 2021. The percentage of consumers sourcing "all" their respective products legally was greater in 2021 than 2020 across all products. Legal sourcing varied by frequency of use: weekly or more frequent consumers were more likely to source "some" (versus "none") of their products legally versus less frequent consumers. Legal sourcing also varied by province, with a lower likelihood of legal sourcing in Québec of products whose legal sale was restricted (e.g., edibles). CONCLUSION: Legal sourcing increased over time, demonstrating progress in the transition to the legal market for all products in the first three years of legalization in Canada. Legal sourcing was highest for drinks and oils and lowest for solid concentrates and hash.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".