Assessing options for cannabis law reform: A Multi-Criteria Decision Analysis (MCDA) with stakeholders in New Zealand
Bibliographic record
Abstract
BACKGROUND: A number of jurisdictions are considering or implementing different options for cannabis law reform, including New Zealand. Multi-Criteria Decision Analysis (MCDA) helps facilitate the resolution of complex policy decisions by breaking them down into key criteria and drawing on the combined knowledge of experts from various backgrounds. AIMS: To rank cannabis law reform options by facilitating expert stakeholders to express preferences for projected reform outcomes using MCDA. METHODS: A group of cannabis policy experts projected the outcomes of eight cannabis policy options (i.e., prohibition, decriminalization, social clubs, government monopoly, not-for-profit trusts, strict regulation, light regulation, and unrestricted market) based on five criteria (i.e., health and social harm, illegal market size, arrests, tax income, treatment services). A facilitated workshop of 42 key national stakeholders expressed preferences for different reform outcomes and doing so generated relative weights for each criterion and level. The resulting weights were then used to rank the eight policy options. RESULTS: The relative weighting of the criteria were: "reducing health and social harm" (46%), "reducing arrests" (31%), "reducing the illegal market" (13%), "expanding treatment" (8%) and "earning tax" (2%). The top ranked reform options were: "government monopoly" (81%), "not-for-profit" (73%) and "strict market regulation" (65%). These three received higher scores due to their projected lower impact on health and social harm, medium reduction in arrests, and medium reduction in the illegal market. The "lightly regulated market" option scored lower largely due its projected greater increase in health and social harm. "Prohibition" ranked lowest due to its lack of impact on reducing the number of arrests or size of the illegal market. CONCLUSION: Strictly regulated legal market options were ranked higher than both the current prohibition, and alternatively, more lightly regulated legal market options, as they were projected to minimize health and social harms while substantially reducing arrests and the illegal market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".