‘Preparing ourselves to become an international organization’: Thailand Tobacco Monopoly’s regional and global strategies
Bibliographic record
Abstract
The Thailand Tobacco Monopoly (TTM) controlled the country's tobacco industry from its formation in the 1940s, until the government dropped restrictions on imported cigarettes in the late 1980s in response to pressure from the United States. The TTM has since competed with transnational tobacco companies (TTCs) in a semi-monopoly market in which TTCs have steadily increased their market share. Coupled with a decline in national smoking prevalence, the result of Thailand's stringent tobacco control agenda, the TTM now accounts for a diminishing share of a contracting market. In response, the monopoly has looked to regional trade liberalisation, and proximity to markets with some of the world's highest smoking rates to expand its operations. Expansion strategies have gone largely unrealised however, and the TTM effectively remains a domestic operation. Using TTM publications, market and trade reports, industry publications, tobacco industry documents and other resources, this paper analyses TTM expansion strategies, and the limited extent to which they have been achieved. This inability to expand its operations has left the monopoly potentially vulnerable to global strategies of its transnational competitors. This article is part of the special issue 'The Emergence of Asian Tobacco Companies: Implications for Global Health Governance'.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".