Cannabis Legalization in Canada and Combatting the Illicit Cannabis Market
Bibliographic record
Abstract
Despite the elimination of the illegal cannabis market being a fundamental element to cannabis legalization, as outlined in the Cannabis Act (S.C. 2018, c. 16), the Canadian federal government's efforts have had a mild impact in mitigating its influence. In analyzing possible contributions to the illicit sector's sustainability, these factors are in large part due to the federal government failing to understand consumer behaviours in recreational cannabis usage, as evidenced by lack of accommodation for frequent users, pricing and quality of legal cannabis compared to illegal cannabis, alongside faulty enforcement of cannabis law due to issues of conciseness and discretionary powers between the police and federal government. However, there are some suggestions that could better help the federal government in achieving its goal of combatting the illegal cannabis market. The potential amendments to cannabis policy include lowering tax on legal cannabis to better compete with illegal cannabis at the market level while refocusing on addressing public health concerns through promoting the safety of legal cannabis, controlling accessibility of legal cannabis, and providing public education regarding cannabis consumption. If done correctly, each amendment made would address the flaws within cannabis policy, allowing the Canadian federal government to combat the illegal cannabis market more efficiently.
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".