A Comparative Legal Study of Indonesia’s Narcotics Law No. 35 of 2009 and Canada’s Cannabis Regulations SOR/2018-144 on Medical Cannabis Regulation
Bibliographic record
Abstract
Cannabis has long been subject to strict legal control due to its psychoactive properties and potential for abuse, while at the same time attracting increasing attention for its potential medical applications. This study examines the legal regulation of medical cannabis through a comparative analysis of Indonesia’s Law Number 35 of 2009 on Narcotics and Canada’s Cannabis Regulations SOR/2018-144. The research addresses two main issues: first, how medical cannabis is regulated under the respective legal frameworks of Indonesia and Canada; and second, how Indonesia’s institutional approach, particularly through the National Narcotics Agency, responds to medical cannabis needs in comparison with Canada’s regulatory model. This study employs normative legal research using statutory and comparative approaches, supported by legal literature and relevant institutional data. The findings indicate that Indonesia adopts a prohibition-based approach by classifying cannabis as a Schedule I narcotic, thereby excluding its use for medical purposes on the basis of abuse prevention and public health protection. In contrast, Canada recognizes the medical use of cannabis and regulates it through a comprehensive licensing, distribution, and supervision system involving healthcare professionals. The comparative analysis highlights fundamental differences in legal priorities, regulatory design, and institutional responses, demonstrating how public health considerations and risk management are addressed differently within each legal system. These findings provide insight into the regulatory implications of divergent legal approaches to medical cannabis within contemporary narcotics law.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".