Decriminalization of drug use
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the literature on decriminalization of drug use from 2016 to 2017 and suggest the way forward. RECENT FINDINGS: The systematic review of the literature on decriminalization resulted in seven articles that discuss decriminalization as compared with 57 published articles on legalization. Decriminalization of drug use did not have an effect on the age of onset of drug use and the prices of drugs did not decrease after the implementation of drug decriminalization. Policy-based studies on decriminalization suggest shifting from criminal sanctions to a public health approach, which was endorsed by the United Nations (UN) that viewed drug addiction as a preventable and treatable health disorder. One study preferred decriminalization only for cannabis and cautioned against regulating cannabis like alcohol. Another study indicated that general medical practitioners in Ireland did not favour the decriminalization of cannabis. SUMMARY: Scientific evidence supporting drug addiction as a health disorder and the endorsement by the UN strengthen the case for decriminalization. However, studies reporting on the positive outcomes of decriminalization remain scarce. The evidence needs to be more widespread in order to support the case for decriminalization. Furthermore, the endorsement by the UN needs to be acted upon by individual member states.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".