The dark side of marketing seemingly “Light” cigarettes: successful images and failed fact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 it