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Record W2090738060 · doi:10.1093/eurpub/ckl041

Movie Moguls: British American Tobacco's covert strategy to promote cigarettes in Eastern Europe†

2006· article· en· W2090738060 on OpenAlexaff
Eric LeGresley, Monique E Muggli, R. Douglas Hurt

Bibliographic record

VenueEuropean Journal of Public Health · 2006
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Tobacco Research Unit
FundersNational Cancer InstituteNational Institutes of HealthBritish American Tobacco
KeywordsTobacco industryTrademarkConventionDiversification (marketing strategy)Tobacco productBusinessTobacco controlAdvertisingCovertTobacco in AlabamaProduct (mathematics)Promotion (chess)MarketingPolitical scienceMedicineLawTobacco harm reductionPublic healthPoliticsEnvironmental health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.316
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2006
Admission routes1
Has abstractyes

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