Relationship between constituent labelling and reporting of tar yields among smokers in four countries
Bibliographic record
Abstract
Countries have adopted different approaches to disseminating cigarette tar, nicotine, and carbon monoxide (CO) levels to consumers, with some (e.g. EU member states, Canada, Australia, but not the United States) requiring disclosure of results from the International Organization for Standardization (ISO) test method on packs. Cross-country comparisons can provide insight into how smokers use yields when information is presented differently. We examined whether smokers in four different countries could recall the tar yield of their brand of cigarettes, using data from the third wave of the International Tobacco Control Four Country Survey (ITC-4). Of current smokers in the United States, Canada, Australia and the United Kingdom, 33.6% gave a numeric response when asked to report the tar yield of their brand, whereas 66.4% responded 'I don't know.' American participants (9.2%) were less likely than Canadian (28.0%), UK (36.5%) or Australian (68.2%) smokers to give an answer, even after controlling for sociodemographic and smoking behaviour factors. Constituent labelling policies can affect whether smokers report a tar yield for their cigarette brand. Pack labelling appears to be useful for conveying information about cigarettes to smokers; however, there is an urgent need to develop more meaningful information on toxic constituents of cigarette smoke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".