Making sense of <i>pot</i> : conceptual tools for analyzing legal cannabis policy discourse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it