Consumer understanding of cigarette emission labelling
Bibliographic record
Abstract
The optimal way to display constituent levels (e.g. tar) on tobacco packaging has not received adequate attention but has important policy implications. Adult smokers and non-smokers (n = 836) were surveyed in France using Computer Assisted Personal Interviewing to assess perceptions of constituent levels displayed numerically (brand-specific tar and nicotine numbers from smoking machines and the current format in European Union), descriptively (a short sentence describing chemicals and their health effects but without any brand-specific numbers) or as a pack insert (a card placed on the inside of the pack describing the presence of chemicals and their health effects in more detail, as well as information on cessation). We also assessed perceptions of identically packaged cigarettes differing only on nicotine levels. Displaying information regarding ingredients either descriptively or on pack inserts was perceived as more comprehensible and informative than displaying them numerically. Numeric yields were associated with false beliefs: almost half the sample perceived packs with lower nicotine levels (0.8 mg vs. 0.9 mg) to be safer.
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 | 0.008 | 0.000 |
| 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 | 0.001 |
| 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".