Cannabispolitik zwischen Public Health, potenziellen Steuereinnahmen und wirtschaftlichen Interessen: zwei Fallbeispiele
Bibliographic record
Abstract
Derzeit werden in vielen Ländern Änderungen der staatlichen Haltung zum Thema Cannabis diskutiert. Dabei stehen drei Fragestellungen im Vordergrund: Führen Lockerungen der Verfügbarkeit zu mehr Konsum und höherer Krankheitslast, und wenn ja, in welchem Ausmaß? Treffen die Vorhersagen zu vermehrten Steuereinnahmen zu? Führt Legalisierung automatisch zu einer Kommerzialisierung von Cannabisverkauf, und damit zu einem weiteren Anstieg von cannabis- bedingten Problemen? Aufgrund empirischer Forschung in verschiedenen Ländern wird versucht, diese Fragestellungen zu beantworten. Dabei werden die Beispiele von Kanada und Thailand vertieft behandelt, um die Dynamik der derzeitigen Entwicklungen bei einem Hochlohnland und einem Land mit mittlerem Einkommen genauer zu untersuchen. Publication History Article published online: 30 August 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".