Cannabis Policy as Harm Reduction: Polymorphic Models of Responsible Regulation
Bibliographic record
Abstract
Cannabis policy is evolving around the world. While cannabis legalization is perhaps inevitable, responsible regulation is not. Canada provides a unique case study. This paper explores five regulatory models that guide contemporary cannabis policy, organized around public safety, public health, medicinal and therapeutic models, commerce, and racial justice. First, we assess each by focusing on fundamental assumptions, operational goals, and practical outcomes. Next, we consider the impacts of each of these models by exploring significant categories of cannabis policy-based harm. Third, we attempt to reconcile tensions between commerce and control, liberty and safety, and justice and fairness. By re-aligning regulatory cannabis models, we focus on access, equity, and tolerance, re-conceiving public safety, and explicitly committing to consent as central to cannabis diversion programs. Finally, in place of singular governance models, we propose several intermediate polymorphic policy reforms to inform this re-alignment.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.052 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".