The globalisation strategies of five Asian tobacco companies: a comparative analysis and implications for global health governance
Bibliographic record
Abstract
The global tobacco industry, from the 1960s to mid 1990s, saw consolidation and eventual domination by a small number of transnational tobacco companies (TTC). This paper draws together comparative analysis of five case studies in the special issue on 'The Emergence of Asian Tobacco Companies: Implications for Global Health Governance.' The cases suggest that tobacco industry globalisation is undergoing a new phase, beginning in the late 1990s, with the adoption of global business strategies by five Asian companies. The strategies were prompted foremost by external factors, notably market liberalisation, competition from TTCs and declining domestic markets. State protection and promotion enabled the industries in Japan, South Korea and China to rationalise their operations ahead of foreign market expansion. The TTM and TTL will likely remain domestic or perhaps regional companies, JTI and KT&G have achieved TTC status, and the CNTC is poised to dwarf all existing companies. This global expansion of Asian tobacco companies will increase competition which, in turn, will intensify marketing, exert downward price pressures along the global value chain, and encourage product innovation. Global tobacco control requires fuller understanding of these emerging changes and the regulatory challenges posed by ongoing globalisation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".