The devil is in the detail: tobacco industry political influence in the Dutch implementation of the 2001 EU Tobacco Products Directive
Bibliographic record
Abstract
INTRODUCTION: The Dutch implementation of the black border provision in the 2001 European Union Tobacco Products Directive (TPD) is studied to examine the implications of tobacco industry involvement in the implementation phase of the policy process. METHODS: A qualitative analysis was conducted of Dutch government documents obtained through Freedom of Information Act requests, triangulated with in-depth interviews with key informants and secondary data sources (publicly available government documents, scientific literature, and news articles). RESULTS: Tobacco manufacturers' associations were given the opportunity to set implementation specifications via a fast-track deal with the government. The offer of early implementation of the labelling section of the TPD was used as political leverage by the industry, and underpinned by threats of litigation and arguments highlighting the risks of additional public costs and the benefits to the government of expediency and speed. Ultimately, the government agreed to the industry's interpretation, against the advice of the European Commission. CONCLUSIONS: The findings highlight the policy risks associated with corporate actors' ability to use interactions over technical product specifications to influence the implementation of health policy and illustrate the difficulties in limiting industry interference in accordance with Article 5.3 of the Framework Convention on Tobacco Control (FCTC). The implementation phase is particularly vulnerable to industry influence, where negotiation with industry actors may be unavoidable and the practical implications of relatively technical considerations are not always apparent to policymakers. During the implementation of the new TPD 2014/40/EU, government officials are advised to take a proactive role in stipulating technical specifications.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".