Emerging Attitudes Regarding Decriminalization: Predictors of Pro-Drug Decriminalization Attitudes in Canada
Bibliographic record
Abstract
Canada and the United States have recently evaluated the decriminalization of drugs as multiple provinces and states put motions forward to consider drug decriminalization legislation. The influence of factors such as demographics, substance use, perceived substance use risk, and personality have not been widely studied in predicting attitudes toward drug decriminalization. A total of 504 participants were drawn from university ( n = 269, 53.37%) and community samples ( n = 235, 46.63%) through online social media groups and posts (i.e., Facebook, Twitter, Reddit, etc). Analyses indicated that male gender, single or non-married relationship status, living outside of Atlantic Canada, higher problematic alcohol use scores, lower Extraversion, higher Open-mindedness, and lower perceived risk of using substances emerged as significant predictors of support for drug decriminalization. These findings have important implications as public attitudes toward a substance influence drug policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".