Measuring the capacity to combat illicit tobacco trade in 160 countries
Bibliographic record
Abstract
BACKGROUND: Illicit trade of tobacco negatively affects countries' tobacco control efforts. It leads to lower tobacco prices and makes tobacco products more accessible to vulnerable populations. In this study, we constructed an illicit tobacco trade index, which measures the structural and institutional capabilities of 160 countries in addressing illicit tobacco trade. We collected the most recent and best available data on general governance, tobacco control policies, and trade and customs practices. RESULTS: Singapore, New Zealand, Finland and Sweden lead countries with the most favorable illicit tobacco trade score. We observed a positive relationship between illicit tobacco trade scores and Gross National Income (GNI) per capita and a negative relationship with the share of illicit tobacco trade to total tobacco consumption. CONCLUSIONS: The capability to combat illicit trade varies across countries. However, on average, low and middle-income countries (LMICs) are less capable of addressing illicit tobacco trade as suggested by the lower illicit tobacco trade index score. The lower index score in low and middle-income countries was mainly driven by low scores in tobacco control policies and trade and customs practices and conditions. Our study reinforces the importance for LMICs to adopt the WHO's Protocol to Eliminate Illicit Tobacco Trade Products, particularly committing to treaty obligations and investing on track and trace system and other customs reforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".