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Record W2584339707 · doi:10.1080/17441692.2016.1273367

KT&G: From Korean monopoly to ‘a global name in the tobacco industry’

2017· article· en· W2584339707 on OpenAlexaff
Kelley Lee, Lucy Gong, Jappe Eckhardt, Chris Holden, Sungkyu Lee

Bibliographic record

VenueGlobal Public Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsSimon Fraser University
FundersNational Cancer Institute
KeywordsMonopolyTobacco industryGlobalizationTobacco controlCompetition (biology)BusinessMarket shareDomestic marketInternational tradeCorporate governanceMarket economyEconomicsPolitical scienceMarketingPublic healthFinanceMedicine

Abstract

fetched live from OpenAlex

Until the late 1980s, the former South Korean tobacco monopoly KT&G was focused on the protected domestic market. The opening of the market to foreign competition, under pressure from the U.S. Trade Representative, led to a steady erosion of market share over the next 10 years. Drawing on company documents and industry sources, this paper examines the adaptation of KT&G to the globalization of the South Korean tobacco industry since the 1990s. It is argued that KT&G has shifted from a domestic monopoly to an outward-looking, globally oriented business in response to the influx of transnational tobacco companies. Like other high-income countries, South Korea has also seen a decline in smoking prevalence as stronger tobacco control measures have been adopted. Faced with a shrinking domestic market, KT&G initially focused on exporting Korean-manufactured cigarettes. Since the mid-2000s, a broader global business strategy has been adopted including the building of overseas manufacturing facilities, establishing strategic partnerships and acquiring foreign companies. Trends in KT&G sales suggest an aspiring transnational tobacco company poised to become a major player in the global tobacco market. This article is part of the special issue 'The emergence of Asian tobacco companies: Implications for global health governance'.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.131
GPT teacher head0.410
Teacher spread0.279 · 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.

Study designNot applicable
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

Citations10
Published2017
Admission routes1
Has abstractyes

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