The Efficiency of Marijuana Legalization: Uruguay versus Canada
Bibliographic record
Abstract
ABSTRACT The legalization of cannabis has been a trend to be done by countries because it is seen as beneficial in many ways. Marijuana is seen as a drug which has less negative impacts, or can be said “manageable” hence receiving much support from the society to be legalized in a country. Cannabis legalization, either medical or non medical marijuana, is believed to positively impact the welfare of society. Uruguay and Canada are the first and second countries which legalized non medical marijuana through the establishment of a specific marijuana policy to regulate various activities related to marijuana. However, the established marijuana legalization policy has not necessarily proven to give advantages to society. In addition, the impacts of the legalization itself can not only be seen from the fact that the countries have legalized marijuana, rather how the legalization is regulated in the policy. Thus, this research will analyze how marijuana is legalized in the national policy of Uruguay and Canada, and compare the impacts of the policy towards human welfare through the perspective of Social Welfare Theory by Elizabeth Wickenden.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".