Movie Moguls: British American Tobacco's covert strategy to promote cigarettes in Eastern Europe†
Bibliographic record
Abstract
BACKGROUND: Though the cigarette companies have long publicly denied paying for product placement in films, the documentary evidence from the 1950s-1980s overwhelmingly suggests otherwise. METHODS: Approximately 800,000 pages of previously secret internal corporate British American Tobacco Company documents were reviewed at the Minnesota Tobacco Document Depository from March 2003 through May 2005. Documents were also searched online at the various tobacco document collections between February 2004 and November 2004. RESULTS: A small collection of internal corporate documents from British American Tobacco show that in the late 1990s the company evaluated investing in a movie destined for Eastern Europe. By being an investor, BAT could influence the alteration of the movie script to promote BAT's brands, thus providing marketing opportunities without a clear violation of movie product placement restrictions. CONCLUSION: Future protocols to the WHO Framework Convention on Tobacco Control should seek to curtail more than just payment for tobacco product placement. More restrictive provisions will be needed to hinder creative strategies by the tobacco industry to continue tobacco promotion and trademark diversification through movies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".