Why and How the Tobacco Industry Sells Cigarettes to Young Adults: Evidence From Industry Documents
Bibliographic record
Abstract
OBJECTIVES: To improve tobacco control campaigns, we analyzed tobacco industry strategies that encourage young adults (aged 18 to 24) to smoke. METHODS: Initial searches of tobacco industry documents with keywords (e.g., "young adult") were extended by using names, locations, and dates. RESULTS: Approximately 200 relevant documents were found. Transitions from experimentation to addiction, with adult levels of cigarette consumption, may take years. Tobacco marketing solidifies addiction among young adults. Cigarette advertisements encourage regular smoking and increased consumption by integrating smoking into activities and places where young adults' lives change (e.g., leaving home, college, jobs, the military, bars). CONCLUSIONS: Tobacco control efforts should include both adults and youths. Life changes are also opportunities to stop occasional smokers' progress to addiction. Clean air policies in workplaces, the military, bars, colleges, and homes can combat tobacco marketing.
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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 | 0.018 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".