Smuggling as the “key to a combined market”: British American Tobacco in Lebanon
Bibliographic record
Abstract
OBJECTIVES: To understand the strategy of British American Tobacco (BAT) and other transnational tobacco companies (TTCs) to gain access to the Lebanese market, which has remained relatively closed under monopoly ownership and political instability. METHODS: Analysis of internal industry documents, local language secondary sources and industry publications. RESULTS: TTCs have relied on legal and illegal channels to supply the Lebanese market since at least the 1970s. Available documents suggest smuggling has been an important component of BAT's market entry strategy, transported in substantial quantities via middlemen for sale in Lebanon and neighbouring countries. TTCs took advantage of weak and unstable governance, resulting in uncertainty over the Regie's legal status, and continued to supply the contraband trade despite appeals by the government to cease undermining its revenues. Since the end of the civil war in the early 1990s, continued uncertainty about the tobacco monopoly amid political instability has encouraged TTCs to seek a legal presence in the country, while continuing to achieve substantial sales through contraband. CONCLUSION: Evidence of the complicity of TTCs in cigarette smuggling extends to Lebanon and the Middle East where this trade has especially benefited from weak governance and chronic political instability. The regional nature of TTC strategy supports strong international cooperation under the Framework Convention on Tobacco Control to tackle the problem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".