The Taiwan Tobacco and Liquor Corporation: To ‘join the ranks of global companies’
Bibliographic record
Abstract
Until the late 1990s, the Taiwan Tobacco and Liquor Corporation (TTL) focused almost exclusively on serving the domestic market as a highly protected monopoly. This paper describes how the company has adopted a more outward looking strategy since 2000, with ambitions to become a regional, and eventually global, business by 2021. Drawing on company documents and industry sources, the paper argues that this shift in strategy was a direct reaction to the decline in domestic market share following liberalisation of the Taiwanese tobacco market and adoption of tougher domestic tobacco control measures. Market opening occurred as a result of pressure from the U.S. Trade Representative in the 1980s, as well as World Trade Organization membership in 2002. It is argued that TTL's efforts to globalise operations have been limited by bureaucratic company management and structures, and ongoing political tension between Taiwan and China. However, the relative success of TTL's alcohol branch, and potential détente as the Taiwanese government reaches out to improve relations with China, may provide TTL with new opportunities to achieve its goal of becoming a regional player with global ambitions. 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".