BETTER SAFE THAN SORRY? ETHICAL ISSUES ON THE LEGALIZATION OF RECREATIONAL CANNABIS IN CANADA
Bibliographic record
Abstract
On October 17, 2018, the law of the legalization of recreational cannabis comes into force in Canada. Considered by some to be a marked gesture of irresponsibility and political calculation on the part of the Government of Canada, or applauded by others, this event does not go unnoticed. This paper sets out to present the path to the legalization of cannabis in Canada, the main articles of the law on cannabis, but especially the ethical issues related to this legalization. Indeed, the legalization of cannabis was one of the Liberal Party's flagship promises during the federal election race in Canada in 2016. Having won the elections by becoming a majority in Parliament, the Liberal Party of Canada will have to keep its promise. Yet, there are several ethical issues on the horizon: protecting young consumers or encouraging consumption? Resolving some societal problems related to addiction or their aggravation? Profitability for the government or deficit? Positive or negative impacts on the organizations management? Here are questions whose answers remain to be validated by time. This paper is a work in progress one. It represents a personal reflection of the author. It is not based on a comprehensive literature review and does not claim to be standard scientific research.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.030 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.015 |
| 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".