Availability, retail price and potency of legal and illegal cannabis in Canada after recreational cannabis legalisation
Bibliographic record
Abstract
INTRODUCTION AND AIMS: There is little objective market data on the price or potency of legal and illegal cannabis products following recreational cannabis legalisation. DESIGN AND METHODS: In the 2 months post-legalisation in Canada (November-December 2018), legal and illegal cannabis retailers were identified from government lists and online directories. The store location, price and Δ9-tetrahydrocannabinol (THC) and cannabidiol levels of dried herb and cannabis cookies were collected from retailer websites or Weedmaps. RESULTS: We identified 185 legal retailers (22 online stores, 163 storefronts; 65 government-run stores, 120 private stores) and 944 illegal retailers (791 delivery-only services, 157 storefronts). Relative to legal dried herb, illegal dried herb was lower in price (1 g: $10.23 vs. $11.08, ⅛ oz: $9.37/g vs. $10.88/g, ½ oz: $8.18/g vs. $8.85/g; P < 0.05 for all) and higher in potency (THC: 20.5% vs. 16.1%, cannabidiol: 2.4% vs. 1.7%; P < 0.05 for both). Legal private stores had higher prices for dried herb than government-run stores (1 g: $13.08 vs. $10.89, ⅛ oz: $12.75/g vs. $10.45/g, ½ oz: $10.85/g vs. $8.71/g, 1 oz: $8.54/g vs. $7.22/g; P < 0.05 for all). On average, one cannabis cookie in the illegal market contained 96 mg of THC and cost $1.57 per 10 mg of THC. DISCUSSION AND CONCLUSIONS: In the 2 months post-legalisation, illegal cannabis was less expensive, with higher labelled THC content than legal cannabis, although the scope of these differences was more modest than estimates from other crowdsourced and self-reported data. Direct monitoring of cannabis price and potency from legal and illegal retailers is needed to examine the impact of legalisation over time.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".