Lessons Learned from Legal Regulation of Cannabis
Bibliographic record
Abstract
Only two national governments (Uruguay and Canada), along with more than 21 states in the United States, have fully regulated cannabis markets for adult use, and numerous lessons can be gleaned, including how to transition illegal markets to a legal framework while promoting social justice, development, equity and human rights. This chapter explores four main lessons from cannabis regulation: considering a public health approach as a first step accompanied by a development perspective for cultivating communities; integrating social equity or social justice mechanisms including access to capital, licenses and institutional support; the importance of judicial reforms including expungement of criminal convictions, prisoner release and effective decriminalization; and reducing the risk of corporate capture. Future-focused thinking, research and practice require civil society, academics and policymakers to forecast diverse outcomes that put equity-based objectives at the centre to inform prospective initiatives, particularly at an international level. Rather than importing models from others, governments should be flexible in meeting the needs of its populations, with human rights, health and social justice as cross-cutting goals .
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".