The Relationship between the legal status of drug possession and the criminalization of marginalized drug users: A literature review
Bibliographic record
Abstract
The longstanding association between addiction, crime, and mortality has become increasingly severe in Canada, affecting larger numbers of individuals and communities. Diverse and irreconcilable courses of action have been proposed involving the decriminalization of drug possession, expanded resources to promote recovery from addiction, or both. The current review used the PICOTS method to identify peer-reviewed publications that reported outcomes of reducing the criminal consequences of drug possession and the specific relationship between law reform and the well-being of people who are at greatest risk for poisoning. We separately included notable reports and grey literature discussing outcomes associated with the Portuguese National Drug Strategy. Over 2,500 articles were retrieved from three databases, with six meeting all inclusion/exclusion criteria. An additional five manuscripts were retrieved specific to Portugal. The evidence reviewed indicates that drug decriminalization alone is associated with potential harms to drug users and their communities, and that potential benefits may be realized when law reform is closely coordinated with the provision of evidence-based resources that promote recovery from addiction. The evidence summarized in this review supports careful consideration of the factors necessary to promote social reintegration among people who are at highest risk for drug-related harms, including repeated criminal offending and death.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".