Drug Legalization, Democracy and Public Health: Canadian Stakeholders’ Opinions and Values with Respect to the Legalization of Cannabis
Bibliographic record
Abstract
Abstract The legalization of cannabis in Canada instantiates principles of harm-reduction and safe supply. However, in-depth understanding of values at stake and attitudes toward legalization were not part of extensive democratic deliberation. Through a qualitative exploratory study, we undertook 48 semi-structured interviews with three Canadian stakeholder groups to explore opinions and values with respect to the legalization of cannabis: (1) members of the general public, (2) people with lived experience of addiction and (3) clinicians with experience treating patients with addiction. Across all groups, participants tended to be in favor of legalization, but particular opinions rested on their viewpoint as stakeholders. Clinicians considered the way legalization would affect an individual’s health and its potential for increasing rates of addiction on a larger scale. People with lived experience of addiction cited personal autonomy more than other groups and stressed the need to have access to quality information to make truly informed decisions. Alternatively, members of the public considered legalization positive or negative in light of whether one’s addiction affected others. We elaborate on and discuss how scientific evidence about drug use impact values relates and how can different arguments play in democratic debates about legalization.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.036 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".