Filter presence and tipping paper color influence consumer perceptions of cigarettes
Bibliographic record
Abstract
BACKGROUND: Cigarettes are marketed in a wide array of packaging and product configurations, and these may impact consumers' perceptions of product health effects and attractiveness. Filtered cigarettes are typically perceived as less hazardous and white tipping paper (as opposed to cork) often conveys 'lightness'. METHODS: This study examined cigarette-related perceptions among 1220 young adult (age 18-35) current, ever, and never smokers recruited from three eastern U.S. cities (Buffalo NY, Columbia SC, Morgantown WV). Participants rated three cigarette sticks: two filtered cigarettes 85 mm in length, differing only in tipping paper color (cork versus white), and an unfiltered 70 mm cigarette. RESULTS: Overall, the cork-tipped cigarette was most commonly selected on taste and attractiveness, the white-tipped on least dangerous, and the unfiltered on most dangerous. Current smokers were more likely to select white-tipped (OR = 1.98) and cork-tipped (OR = 3.42) cigarettes, while ever smokers more commonly selected the cork-tipped (OR = 1.96), as most willing to try over the other products. Those willing to try the filtered white-tipped cigarette were more likely to have rated that cigarette as best tasting (OR = 11.10), attracting attention (OR = 17.91), and lowest health risk (OR = 1.94). Similarly, those willing to try cork tipped or unfiltered cigarettes rated those as best testing, attracting attention, and lowest health risk, respectively. CONCLUSIONS: Findings from this study demonstrate that consumer product perceptions can be influenced by elements of cigarette design, such as the presence and color of the filter tip.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".