Introduction To Symposium on Drug Decriminalization, Legalization and International Law
Bibliographic record
Abstract
The UN Drug Conventions have nearly universal membership. For decades, the Conventions were widely interpreted as requiring signatory countries to criminalize the cultivation, distribution, sale, and possession of the substances that the Conventions listed in their most restricted schedules. These schedules included cannabis. However, over the past few years, countries around the globe have decriminalized or legalized the personal use of cannabis for medical and recreational purposes. A number of states – including Uruguay, Canada, and eleven states in the United States – have established new regulatory frameworks for governing legal cannabis markets. This symposium explores the implications of the current proliferation of cannabis liberalization reforms for the international drug regime, and for the development of international law and local practice more broadly. Are decriminalization and legalization reforms in conflict with states’ treaty obligations, in letter or in spirit? Is this a moment of transformation for the international drug regime, or an existential threat to its future? While of great practical importance, these dynamics also bear more broadly on questions of how regimes interact (such as drug prohibition, human rights, and public health regimes), how treaties and international institutions evolve and change, and how domestic politics, law, and practice bear on the development and transformation of international law.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".