Framing decriminalization: A mixed-methods study on media narratives, government resources, misinformation, and public support of British Columbia’s drug decriminalization policy
Bibliographic record
Abstract
INTRODUCTION: British Columbia's (BC) three-year drug decriminalization policy-introduced in January 2023 and amended just over a year later in 2024-had multiple goals, including reducing drug use stigma, shifting perceptions of drug use from a criminal to a health issue, and improving health outcomes for people who use drugs. As part of the policy, the BC government was required to implement public education tools to raise awareness and build understanding of the policy. However, little is known about the scope or impact of these public education efforts or how the information environment shaped public perceptions and attitudes toward the policy. To address these gaps, this study examines: 1) how BC's decriminalization policy was communicated and represented across government and media sources, and 2) how exposure to these information sources influenced public support and perceptions of safety. METHODS: This mixed-methods study analyzed 98 government resources, 301 media articles, and a cross-sectional public opinion survey of 1200 BC residents. Content analyses of government resources and media articles examined government resource and media source intent, misinformation, misleading narratives, and perspectives, while the public opinion survey assessed information exposure, policy support, and perceived safety. RESULTS: Approximately one-quarter of all sources were government resources, and among those with publication dates, only 13 % were released prior to the policy's implementation and 9 % contained misinformation, representing a missed opportunity for expectation-setting and public education. In contrast, 34 % of media articles contained misinformation, commonly misrepresenting the policy's intent and linking decriminalization to increased crime, disorder, and public drug use. Survey findings showed no significant associations between specific information sources and outright opposition. However, respondents exposed to multiple information sources were significantly less likely to report a neutral stance compared to support (OR [95 % CI]: 0.31 [0.15-0.65]). Those accessing official/academic sources or multiple sources were also less likely to feel less safe (OR [95 % CI]: 0.22 [0.07-0.71] and 0.43 [0.24-0.78]). CONCLUSION: These findings highlight critical gaps in government communication and the dominance of misrepresentative media framing in shaping public attitudes. Effective drug policy requires not only legislative change but also proactive, coordinated, and sustained public education strategies to counter misinformation, reduce stigma, and build lasting support.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".