Bibliographic record
Abstract
The hardening hypothesis has intuitive and common sense appeal: in jurisdictions that have implemented evidence-based tobacco control policies, the smokers who have a relatively easy time quitting will quit, and as the future unfolds there will be an increasing proportion of remaining smokers who cannot quit and are more resistant to quitting than smokers in the past. In this context, ‘hardening’ is a measure of a group, or population, over time. If a group or population is hardening, it implies that the proportion of smokers who are ‘hard core’ is increasing. ‘Hard core’ was first used in the peer reviewed literature in relation to smokers by Lichtenstein and Keutzer in 1973, in their review of how psychological research could be applied in smoking cessation clinics.1 Following this initial intimation of a hard core smoker, the term popped up again in the late 1980s,2–5 and since then the literature on hard core smokers has grown, although the number of papers that have empirically examined this topic remains limited. The bottom line from the body of evidence to date is that smokers classified as hard core represent only a very small minority of all smokers (in selected high income countries for which data have been available), and that ‘hardening of the target’ is still a long way off. Even analyses focused on the individual level find only a handful of subgroups where there is a suggestion of hardening. Cross-sectional data show that the lower the prevalence of smoking, the lower the average number of cigarettes smoked per day and the lower the percentage of smokers who smoke within 30 min of waking.6 Similarly, …
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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".