Transnational Tobacco Companies and New Nicotine Delivery Systems
Bibliographic record
Abstract
While the public health community has focused on the harm-reduction potential of new nicotine delivery systems (NNDSs) and, conversely, their potential for impeding overall efforts to prevent and reduce tobacco use, limited analysis has been conducted on the role of leading transnational tobacco companies (TTCs) in this rapidly growing market. Following aborted efforts during the 1980s and 1990s to develop reduced-risk products, TTCs have heavily invested in selected NNDS products since 2010 via acquisitions and internal research and development. This article catalogs and analyzes the patterns of investment in NNDSs by leading TTCs over time, and identifies differences in the companies' approaches to NNDS product acquisition and development in specific markets globally. This analysis raises important questions regarding the companies' intent, which appears to be to sustain, rather than replace, cigarette sales, and to increase their influence and credibility with respect to NNDS policy and regulation. We identify the need for greater public health vigilance and research to understand and respond to the increasingly significant role of NNDSs in TTCs' global business strategies, to ensure that NNDSs advance, rather than hinder, tobacco control efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".