Making sense of <i>pot</i> : conceptual tools for analyzing legal cannabis policy discourse
Bibliographic record
Abstract
In the last decade, there has been a significant surge in cannabis legalization, with Uruguay (2013), Canada (2018) and 19 U.S. states (2012-2022) having developed recreational cannabis policies. A growing literature analyzes legalization from a policymaking or public health standpoint. Yet only few studies have explored its discursive component . This article contributes to filling this gap by developing conceptual tools for cannabis policy discourse analysis. I first examine the history of cannabis policy in North America and find two main discursive clusters, i.e., moral and epistemic discourse. I then discuss existing typologies of cannabis regulation models and select that of Beauchesne, which distinguishes between three models: prohibition 2.0, public health and harm reduction, and commercialization. At the intersection of discursive clusters and these regulation models, I identify six mutually exclusive frames of cannabis policy: moral panic, medical/health, reparations/vulnerabilities, harm reduction/risk mitigation, laissez-faire/liberalism, and illicit market/revenue.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".