Comparative Cannabis: Approaches to Marijuana Agriculture Regulation in the United States and Canada
Bibliographic record
Abstract
The United States and Canada may be friends and allies, but the two countries' approaches to the regulation of marijuana agriculture have not evolved in tandem. On the contrary, their respective paths toward legalization and regulation of marijuana agriculture are remarkably divergent. In the United States, where marijuana remains a federally prohibited and tightly-controlled substance, legalization and regulation have remained the province of state legislatures and their administrative agencies for decades. In Canada, a succession of court cases paving the way toward medicinal marijuana use has prompted the federal government to develop a national framework committed to "legalize, regulate, and restrict access" to marijuana.\nMany jurisdictions attempting to regulate (or exploring the possibility of regulating) the marijuana industry struggle to address the first step in the supply chain agriculture. This essay will compare and contrast the experiences of the United States and Canada in the regulation of marijuana agriculture. It is evident that there is more than one regulatory approach that can provide a safe and sustainable product to consumers while promoting equity among farmers. Nonetheless, the trials and tribulations of pioneering governments can illuminate the pitfalls, consequences, and drawbacks policymakers are likely to encounter in the future. [excerpt]
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".