Measuring drug policy evolution: A cross-country analysis
Bibliographic record
Abstract
Drug policies significantly impact public health and criminal justice outcomes, yet quantitative tools for systematically comparing approaches across jurisdictions remain limited. This paper uses a state-of-the-art comparative law method - leximetrics - to construct the Illicit Drugs Policy Indexes (IDPI), a valuable resource for assessing the evolution of drug policies over time within a specific country as well as across countries. The IDPI consists of a set of indexes corresponding to multiple dimensions of drug policy, including laws around consumption, possession and traffic. These indexes examine illicit drug laws and policies across seven countries: Australia, Canada, France, Italy, Netherlands, Portugal and the United Kingdom, over a timeframe of twenty years from 1996 to 2016. Our results identify significant turning points in the evolution of laws regarding drugs, often indicating a shift towards less criminal-oriented approaches. Moreover, the paper identifies the countries which progressed more in that direction, over time. The underlying IDPI methodology provides policymakers and researchers with a standardized framework for evidence-based drug policy evaluation and reform, adaptable across jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".