Comparison of the effects of marijuana legalization on mental health in selected countries
Bibliographic record
Abstract
The topic of legalizing marijuana for recreational use is becoming increasingly relevant over time, not only worldwide but also in the Czech Republic. This trend began with the legalization of recreational marijuana in the United States, specifically in the states of Colorado and Washington in 2012. Subsequently in 2013 Uruguay became the first country to legalize marijuana throughout its entire territory. Since then, other U.S. states and countries such as Canada and Malta have joined the legalization movement. The most interesting development from the perspective of the Czech Republic is in Germany, where the legalization of cultivation and possession of marijuana for recreational use is planned from the year 2024. This, coupled with the growing support from experts and politicians advocating for marijuana legalization in the Czech Republic, could lead to the adoption of similar legislation. The legalization of marijuana raises a series of questions that need to be addressed before such a step is taken. Among the most common concerns associated with marijuana legalization are its impact on mental health and the potential increase in marijuana use among adolescents due to easier access to the drug. To answer these questions, it is essential to compare existing models of marijuana legalization...
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".