The “risk object” of cannabis edibles: perspectives from young adults in Canada
Bibliographic record
Abstract
Young adults are the most prominent users of cannabis in Canada, which was legalised for recreational use in 2018. Edibles are a highly popular form of cannabis delivery for this age group, yet little qualitative research explores young adult perspectives on edibles, including how edibles function socially for them, or are viewed in terms of risk. This study fills this research gap, conducting focus groups with 57 young adults (ages-18–24) in Calgary, Alberta, Canada to explore the ‘risk object’ of cannabis edibles. Findings reveal that delivery mechanism of cannabis – that is, cannabis in food form compared to smoked/inhaled – significantly shifts perceptions of risk by young adults. Edibles were viewed as less risky than smoking cannabis, and participants stated they were more willing to consume edibles than in any other form (and especially in social settings). Risks associated with edibles were primarily short-term/related to over-consumption and were largely mitigated by legalisation. However, participants also emphasised risks for others (including children and inexperienced consumers). Overall, the ‘risk object’ of cannabis edibles shifts depending on the audience considered. Participants described individual consumers as shouldering primary responsibility for managing risks related to cannabis edibles, despite manufacturer and government roles in regulating product quality and protecting vulnerable populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".