Prohibition vs. Legalization of Marijuana: The Canadian Context on Legalization Policy
Bibliographic record
Abstract
Prohibitive drug laws have been in place for over 100 years yet a global movement is emerging that argues that criminalization is a poor policy option to address the War on Drugs. Cannabis is one of the most widely used illegal drugs worldwide with roughly 4% of the global adult population being illegal drug users. Prohibition has resulted in the creation of illegal markets, and produced greater negative consequences associated with controlling the production, distribution and consumption of the product. Another issue is the effect that prohibition has on government spending in terms of enforcement costs, court costs, and incarceration. As of 2012, the cost of keeping an inmate incarcerated in Canada is $117,000. Roughly $1 billion is spent annually on drug enforcement in Canada. Policies supporting the prohibition of marijuana are based in large part on the notion that consuming marijuana is both a public safety concern, and detrimental to an individual's health, yet there is little research to back this up. From a revenue standpoint, Canada is losing out on a large amount of money that could go to better serve our population rather than remain in the hands of an illegal market. In the last six months the success of legalization in Colorado has legitimized the notion that Canada should act on reforming its marijuana policy. Although there are some minor health implications if marijuana were to be legalized, it is far less detrimental when compared to alcohol and tobacco consumption. This paper recommends that marijuana be legalized and regulated in Canada. Taxing marijuana at multiple stages can produce a large amount of revenue for the federal and provincial governments. Mandatory licensing for cultivators and retailers will ensure effective regulation. From a tax policy perspective, Canada has the potential to generate between $4 to $8 billion dollars in revenue.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".