10.1177/1090198105275047Health Education & Behavior (June 2005)Nathanson / Collective Actors and Corporate Targets Collective Actors and Corporate Targets in Tobacco Control: A Cross-National Comparison
Bibliographic record
Abstract
Cross-national comparative analysis of tobacco control strategies can alert health advocates to how opportu-nities for public health action, types of action, and probabilities for success are shaped by political systems and cultures. This article is based on case studies of tobacco control in the United States, Canada, Britain, and France. Two questions are addressed: (a) To whom were the dangers of smoking attributed? and (b) What was the role of collective action—grassroots level organization—in combating these dangers? Activists in Canada, Britain, and France moved earlier than the United States did to target the tobacco industry and the state. Locally based advocacy centered on passive smoking has been far more important in the United States. The author con-cludes that U.S.-style advocacy has played a major role in this country’s smoking decline but is insufficient in and of itself to change the corporate practices of a wealthy and politically powerful industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.957 | 0.916 |
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".