Tobacco industry globalization and global health governance: towards an interdisciplinary research agenda
Bibliographic record
Abstract
Shifting patterns of tobacco production and consumption, and the resultant disease burden worldwide since the late twentieth century, prompted efforts to strengthen global health governance through adoption of the Framework Convention on Tobacco Control. While the treaty is rightfully considered an important achievement, to address a neglected public health issue through collective action, evidence suggests that tobacco industry globalization continues apace. In this article, we provide a systematic review of the public health literature and reveal definitional and measurement imprecision, ahistorical timeframes, transnational tobacco companies and the state as the primary units and levels of analysis, and a strong emphasis on agency as opposed to structural power. Drawing on the study of globalization in international political economy and business studies, we identify opportunities to expand analysis along each of these dimensions. We conclude that this expanded and interdisciplinary research agenda provides the potential for fuller understanding of the dual and dynamic relationship between the tobacco industry and globalization. Deeper analysis of how the industry has adapted to globalization over time, as well as how the industry has influenced the nature and trajectory of globalization, is essential for building effective global governance responses. This article is published as part of a thematic collection dedicated to global 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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".