“Legalize It!?” – Opportunities and Challenges for the Regulation of Cannabis under European Law: Is Legalisation Legal?
Bibliographic record
Abstract
Following similar developments in other parts of the world (e.g. Uruguay, Canada, United States, Thailand), several countries in the EU are questioning or openly challenging the prohibitionist paradigm that has so far dominated international drug control law. Possibly the most far-reaching approach is contained in the concept paper (Eckpunktepapier) adopted by the German Federal Government in October 2022, which would provide for the comprehensive regulation of cannabis for recreational use from “seed to sale”. While the legality of this approach under public international and EU law has been called into question, this article shows that a responsible regulation of cannabis is not only desirable from a policy perspective but also legally feasible.
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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 | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".