Annotated Bibliography to Accompany Anderson, Pollay, & Ling, "Taking Ad-Vantage of Lax Advertising Regulations: Reassuring and Distracting Health-Concerned Smokers" (Social Science & Medicine, 2006)
Bibliographic record
Abstract
We explored the evolution from cigarette product attributes to psychosocial needs in advertising campaigns for low-tar cigarettes. Analysis of previously secret tobacco industry documents and print advertising images indicated that low-tar brands targeted smokers who were concerned about their health with advertising images intended to distract them from the health hazards of smoking. Advertising first emphasized product characteristics (filtration, low tar) that implied health benefits. Over time, advertising emphasis shifted to salient psychosocial needs of the target markets. A case study of Vantage in the USA and Canada showed that advertising presented images of intelligent, upward-striving people who had achieved personal success and intentionally excluded the act of smoking from the imagery, while minimal product information was provided.\n This illustrates one strategy to appeal to concerned smokers by not describing the product itself (which may remind smokers of the problems associated with smoking) but instead using evocative imagery to distract smokers from these problems. Current advertising for potential reduced-exposure products (PREPs) emphasizes product characteristics, but these products have not delivered on the promise of a healthier alternative cigarette. Our results suggest that the tobacco control community should be on the alert for a shift in advertising focus for PREPs to the image of the user rather than the cigarette. Framework Convention on Tobacco Control-style advertising bans that prohibit all user imagery in tobacco advertising could preempt a psychosocial needs-based advertising strategy for PREPs and maintain public attention on the health hazards of smoking.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.113 |
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".