Normalization and denormalization in different legal contexts: Comparing cannabis and tobacco
Bibliographic record
Abstract
Aims: This study provides an examination of normalization trends associated with the use of cannabis and tobacco, and whether and to what extent health concerns and legal contexts appear to modify the tolerance displayed to users. Methods: Data for this paper are drawn from a mixed methods interview study involving 202 respondents who reported being regular users of cannabis (alone n = 100 or in conjunction with tobacco n = 67) or tobacco only users (n = 35), in four Canadian cities (Halifax, Montreal, Toronto and Vancouver). Findings: While participants commonly attributed serious health risks to the use of tobacco, cannabis was viewed as relatively low risk. All groups described cannabis laws as too punitive, while most agreed with the regulatory controls for tobacco. Drawing on norms around appropriate context for use, cannabis users illustrate the expansion of normalization, with varying degrees of acceptability in different spaces. In contrast, tobacco users’ heightened awareness of the dangers of smoking leads them to engage in a reflexive process-limiting appropriate venues and contexts for use. Conclusions: These findings suggest that perceptions of health risk shape users’ experience of normalization (and denormalization) and help to contextualize the larger societal processes where both drugs are in a stage of societal re-evaluation. Much can be learned about the cannabis future from the tobacco past.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".