Bibliographic record
Abstract
On October 17, 2018, Canada legalized the recreational possession and use of cannabis federally under the Cannabis Act. The Cannabis Act states goals of protecting young people from cannabis, reducing and deterring illicit activities in relation to cannabis, and providing the public with access to a supply of legal, quality-controlled cannabis. Despite this, the black market for cannabis has remained strong and persistent, with research indicating that the black market accounted for approximately 71-86% of cannabis sales in the first year of legalization. This paper will explore how and why Canada’s criminal black market for cannabis continues to function after legalization, and what measures can be taken to counteract it. Canada’s illicit black market for cannabis continues to function as the by-product of a reprobate stew of mail-order and traditional cannabis dealers, who operate in a difficult-to-enforce periphery of the Cannabis Act. They continue to flourish by offering cheaper, higher quality, and more available cannabis, functioning as a better-run business outside of the stringent regulatory requirements of the licit market, particularly in packaging and marketing requirements. This paper will recommend that licit retailers and the government must take several decisive steps to combat this. First, amend the Canada Post Corporation Act. Second, be a better business generally by offering lower cost, higher quality cannabis that is consistently available in stores. Third, introduce affordable cannabis options to directly address price-sensitive consumers. Fourth, engage in consumer education. Fifth, loosen marketing restrictions on legal cannabis retailers. Sixth, pass legislation to better utilize the banking and financial sector to trace and flag bank accounts associated with illegal cannabis sales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".