Youth Cannabis use and Legalization in Canada - Reconsidering the Fears, Myths and Facts Three Years In.
Bibliographic record
Abstract
Canada legalized and regulated non-medical cannabis in October 2018, and in the lead up to this policy change much debate was generated around the Federal Government's stated objective of "keeping cannabis out of the hands of children and youth". As Canada moved through the process of passing Bill C-45 (the Cannabis Act), a contentious issue was whether the 'public health approach' to legalization with strict regulation guiding Federal policy would protect young people from accessing cannabis and from the potential harms of use. Now that we are several years post-legalization of cannabis, in this brief commentary we reconsider the arguments made about the potential consequences of legalization for youth, centered on three key concerns: that prevalence would significantly increase, that there would be greater incidence of harms to youth brain development, and that there would be increased presentations of severe mental illnesses associated with cannabis use. We also consider how focusing narrowly on clinical outcomes has neglected the association between criminalization and social inequities, where the burdens are disproportionate for marginalized and racialized youth.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".