Facing the option for the legalisation of cannabis use and supply in New Zealand: An overview of relevant evidence, concepts and considerations
Bibliographic record
Abstract
ISSUES: Non-medical cannabis policies are changing, including towards legalisation-with-regulation frameworks. New Zealand will hold a public referendum on cannabis legalisation in 2020. We reviewed data on cannabis use and health/social harms; policy reform options; experiences with and outcomes of reforms elsewhere; and other relevant considerations towards informing policy choices in the upcoming referendum. APPROACH: Relevant epidemiological, health, social, criminal justice and policy studies and data were identified and comprehensively reviewed. KEY FINDINGS: Cannabis use is common (including in New Zealand) and associated with risks for health and social harms, mainly concentrated in young users; key harms are attributable to criminalisation. 'Decriminalisation' reforms have produced ambivalent results. Existing cannabis legalisation frameworks vary considerably in main parameters. Legalisation offers some distinct advantages, for example regulated use, products and user education, yet outcomes depend on essential regulation parameters, including commercialisation, and policy ecologies. While major changes in use are not observed, legalisation experiences are inconclusive to date, including mixed health and social outcomes, with select harms increasing and resilient illegal markets. It is unclear whether legalisation reduces cannabis exposure or social harms (e.g. from enforcement) for youth. IMPLICATIONS/CONCLUSIONS: No conclusive overall evidence on the outcomes of legalisation elsewhere exists, nor is evidence easily transferable to other settings. Legalisation offers direct social justice benefits for adults, yet overall public health impacts are uncertain. Legalisation may not categorically improve health or social outcomes for youth. Legalisation remains a well-intended, while experimental policy option towards more measured and sensible cannabis control and overall greater policy coherence, requiring close monitoring and possible adjustments depending on setting-specific outcomes.
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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 | 0.033 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".