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Record W2555024457 · doi:10.1080/17441692.2016.1251604

The globalisation strategies of five Asian tobacco companies: An analytical framework

2016· article· en· W2555024457 on OpenAlexaff
Kelley Lee, Jappe Eckhardt

Bibliographic record

VenueGlobal Public Health · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsSimon Fraser University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsTobacco controlGlobalizationTobacco industryConsumption (sociology)Tobacco useCorporate governanceGlobal healthSouth asiaPolitical scienceDevelopment economicsEconomic growthBusinessEnvironmental healthPublic healthMedicineEconomicsHealth carePopulationSociologySocial science

Abstract

fetched live from OpenAlex

With 30% of the world’s smokers, two million deaths annually from tobacco use, and rising levels of tobacco consumption, the Asian region is recognised as central to the future of global tobacco control. There is less understanding, however, of how Asian tobacco companies with regional and global aspirations are contributing to the global burden of tobacco-related disease and death. This introductory article sets out the background and rationale for this special issue on ‘The Emergence of Asian Tobacco Companies: Implications for Global Health Governance’. The article discusses the core questions to be addressed and presents an analytical framework for assessing the globalisation strategies of Asian tobacco firms. The article also discusses the selection of the five case studies, namely as independent companies in Asia which have demonstrated concerted ambitions to be a major player in the world market.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0040.007
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.362
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations16
Published2016
Admission routes1
Has abstractyes

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