Legalizing and Regulating Marijuana in Canada: Review of Potential Economic, Social, and Health Impacts
Bibliographic record
Abstract
Notwithstanding a century of prohibition, marijuana is the most widely used illicit substance in Canada. Due to the growing public acceptance of recreational marijuana use and ineffectiveness of the existing control system in Canada, the issue surrounding legalizing this illicit drug has received considerable public and political attentions in recent years. Consequently, the newly elected Liberal Government has formally announced that Canada will introduce legislation in the spring of 2017 to start legalizing and regulating marijuana. This editorial aims to provide a brief overview on potential economic, social, and public health impacts of legal marijuana in Canada. The legalization could increase tax revenue through the taxation levied on marijuana products and could also allow the Government to save citizens' tax dollars currently being spent on prohibition enforcement. Moreover, legalization could also remove the criminal element from marijuana market and reduce the size of Canada's black market and its consequences for the society. Nevertheless, it may also lead to some public health problems, including increasing in the uptake of the drug, accidents and injuries. The legalization should be accompanied with comprehensive strategies to keep the drug out of the hands of minors while increasing awareness and knowledge on harmful effects of the drug. In order to get better insights on how to develop an appropriate framework to legalize marijuana, Canada should closely watch the development in the neighboring country, the United States, where some of its states viz, Colorado, Oregon, Washington, and Alaska have already legalized recreational use of marijuana.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".