The dark side of marketing seemingly “Light” cigarettes: successful images and failed fact
Bibliographic record
Abstract
OBJECTIVE: To understand the development, intent, and consequences of US tobacco industry advertising for low machine yield cigarettes. METHODS: Analysis of trade sources and internal US tobacco company documents now available on various web sites created by corporations, litigation, or public health bodies. RESULTS: When introducing low yield products, cigarette manufacturers were concerned about maintaining products with acceptable taste/flavour and feared consumers might become weaned from smoking. Several tactics were employed by cigarette manufacturers, leading consumers to perceive filtered and low machine yield brands as safer relative to other brands. Tactics include using cosmetic (that is, ineffective) filters, loosening filters over time, using medicinal menthol, using high tech imagery, using virtuous brand names and descriptors, adding a virtuous variant to a brand's product line, and generating misleading data on tar and nicotine yields. CONCLUSIONS: Advertisements of filtered and low tar cigarettes were intended to reassure smokers concerned about the health risks of smoking, and to present the respective products as an alternative to quitting. Promotional efforts were successful in getting smokers to adopt filtered and low yield cigarette brands. Corporate documents demonstrate that cigarette manufacturers recognised the inherent deceptiveness of cigarette brands described as "Light"or "Ultra-Light" because of low machine measured yields.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".