Promoting Tobacco Use Among Students: The U.S. Smokeless Tobacco Company College Marketing Program
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: From the 1970s-1990s the U.S. Smokeless Tobacco Company (USST) conducted aggressive campaigns to solicit college students to buy their smokeless tobacco (ST) products. The scope, scale, methods, and impact of this youth marketing campaign have yet to be analyzed in the academic literature. STUDY DESIGN: Historical research study. METHODS: Internal industry documents describing the USST campaigns were obtained via the University of California, San Francisco's repository of tobacco company records. Marketing materials were obtained from Stanford University's Research Into the Impact of Tobacco Advertising (SRITA) collection of 657 USST advertisements. RESULTS: USST's College Marketing Program (1978-mid 1980s) sponsored events in some 350 campuses and hired student representatives in at least 175 colleges and universities across America. College representatives were trained to provide free samples to fellow students. Over a typical school year approximately a quarter million Happy Days, Skoal, and Skoal Bandits samples were handed out to undergraduates. USST paid their student representatives well and offered them a variety of incentives based upon sales growth. During the 1990s, USST's Skoal Music program engaged students on campuses and at "spring break" venues such as Daytona Beach. CONCLUSIONS: Targeting of college students on campus was a common tobacco industry practice between the 1940s and early 1960s. From the 1970s through 1990s USST resurrected the method and pursued it with vigor including: distribution of free samples; sponsored events and concerts, branded intramural teams; visits by sports celebrities; logo wearables and merchandise; contests and incentives; and displays and promotions in stores on and surrounding campuses. Laryngoscope, 131:E1860-E1872, 2021.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".