Puffing Away? Explaining the Politics of Tobacco Control in Germany
Bibliographic record
Abstract
Germany is noted within Europe for its weak tobacco control policies and its opposition to European Union tobacco control legislation. In this article, we aim to explain Germany's stance on tobacco control. We review two explanations commonly proposed, namely tobacco industry donations to political parties and the legacy of the Nazis' opposition to smoking, and examine the politics of tobacco control in detail. We suggest that the interplay of numerous factors explains Germany's stance. Aspects of political culture including the Nazi heritage which has resulted in a dearth of public health research and teaching, institutional factors such as the reliance on industry self-regulation facilitated by Germany's system of corporatist policy-making and interest group politics are key. The tobacco industry has also successfully used framing strategies to uphold the social acceptability of smoking and undermine the acceptability of tobacco control in Germany. In addition a phenomenon that we call ‘autarkic epistemic isolation’ explains why so little policy learning from abroad has occurred. We suggest that our multi-factor model has significant explanatory power for Germany's weak stance that has resulted in a long-standing policy equilibrium. Recent events, however, suggest that this equilibrium may now have been punctuated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".