From right or wrong to true or false: Moral and epistemic framing in debates over cannabis policy reformulation
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
Abstract Cannabis legalization is often referred to as a moral issue. However, given the limits of morality policy as a distinct policy subcategory and the contemporary dominance of technocratic politics, one could wonder if it is really framed as such within political institutions. In this article, I ask how moral frames compete and interact with other frames in debates over morality policy. Working with a moral/epistemic dichotomy, I conduct framing analysis on parliamentary debates in Quebec, Ontario, and Maine, which have recently reformulated their cannabis policy. Although trends in framing vary across cases, moral frames are consistently less salient than epistemic frames. Furthermore, a pattern of complementary framing is found, whereby actors combine moral and epistemic frames. Overall, this study shows that cannabis policy is often framed as nonmoral, and that its moral component is nonexclusive. I conclude by discussing some implications of these findings in the post‐legalization landscape. Related Articles Branton, Regina, and Ronald J. McGauvran. 2018. “Mary Jane Rocks the Vote: The Impact of Climate Context on Support for Cannabis Initiatives.” Politics & Policy 46(2): 209–32. https://doi.org/10.1111/polp.12248 . Brekken, Katheryn C., and Vanessa M. Fenley. 2021. “Part of the Narrative: Generic News Frames in the U.S. Recreational Marijuana Policy Subsystem.” Politics & Policy 49(1): 6–32. https://doi.org/10.1111/polp.12388 . Fisk, Jonathan M., Joseph A. Vonasek, and Elvis Davis. 2018. “‘Pot’Reneurial Politics: The Budgetary Highs and Lows of Recreational Marijuana Policy Innovation.” Politics & Policy 46(2): 189–208. https://doi.org/10.1111/polp.12246 .
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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