‘Every space is claimed’: smokers’ experiences of tobacco denormalisation
Bibliographic record
Abstract
Over the past decade, the strategy of 'denormalising' tobacco use has become one of the cornerstones of the global tobacco control movement. Although tobacco denormalisation policies primarily affect people on the lowest rungs of the social ladder, few qualitative studies have explicitly set out to explore how smokers have experienced and responded to these legislative and social changes in attitudes towards tobacco use. Drawing on a qualitative study of interviews with 25 current and ex-smokers living in Vancouver, Canada, this paper examines the ways they interpret and respond to the new socio-political environment in which they must manage the increasingly problematised practice of tobacco smoking. Overall, while not opposed to smoking restrictions per se, study participants felt that recent legislation, particularly efforts to prohibit smoking in a variety of outdoor settings, was overly restrictive and that all public space had increasingly been 'claimed' by non-smokers. Also apparent from participants' accounts was the high degree of stigma attached to smoking. However, although the 'denormalisation' environment had encouraged several participants to quit smoking, the majority continued to smoke, raising ethical and practical questions about the value of denormalisation strategies as a way of reducing smoking-related mortality and morbidity.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| 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".