Legalization of cannabis in Canada—Local media analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Legalization of recreational cannabis is occurring across the United States, with some controversy. To understand the range of issues that can arise when such a policy change is enacted, we examined portrayal of legalization at the local level by studying newspaper articles in Calgary, Alberta, shortly before and after cannabis legalization in Canada. METHOD: We searched the largest-circulation newspaper for cannabis-related items and analyzed for content and slant toward cannabis legalization. RESULTS: Among 165 items, business/economics (70.9% of items) and legalization (69.7%) were most frequent, with health only 29.7%. Across all items, the slant was more approval (44.2%) than disapproval (23.0%). DISCUSSION AND CONCLUSIONS: When cannabis was legalized, the local newspaper focused more on economic aspects of legalization rather than about health issues. Further research can determine the generalizability of the findings to other locales and provide comparison as other similar policy changes roll out. SCIENTIFIC SIGNIFICANCE: The study provides new information on what happens when drug policies are enacted. Documenting the media portrayal of substance use policies is a promising tool.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".