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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".