What will be the impact of legalizing medical cannabis on economic effects in Japan? : analysis of case study in Germany and USA, Canada, Thailand.
Bibliographic record
Abstract
In recent years, cannabis has been legalized all over the world. Countries around the world are turning into cannabis legalization. As a result, in 2018, the WHO Expert Committee on Drug Dependence recommended the redesignation of cannabis, taking into account evidence of medical use (Mayor, 2019). Until today, the medical cannabis (MMJ) program has started in both high- and middle-income countries, including Canada, Colombia, Chile, Germany, Israel, Italy, Jamaica, the Netherlands, Switzerland, Thailand, the United Kingdom, Uruguay, and more than 30 states in the United States. In this study, I will consider the advantages and disadvantages of accepting medical cannabis with several cases of each country. There also be advised how Japanese government on regulating medical cannabis. As a result, it is necessary to discuss the liberalization of cannabis and the use of medical cannabis separately to prevent social turmoil when implementing cannabis-related policies in Japan. States also need to assess how much cannabis use can be regulated to minimize risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".