Bibliographic record
Abstract
“More powerful than vested interests, more subtle than science, political ideology has, in the end, the greatest influence on disease prevention policy.” Sylvia Noble Tesh1 It is widely acknowledged that strong tobacco control policies are a crucial part of a comprehensive approach to reduce the health and economic impacts of tobacco use.2 Legislators, commissioners, and city councillors ultimately determine what policies are enacted and maintained. Yet, we know relatively little about the factors that influence elected officials to support or oppose these policies. Political scientists who traditionally study legislator voting behaviour often include measures of ideology in their analyses. However, health researchers have generally neglected political ideology in their studies of legislative outcomes related to tobacco control. Political ideology includes assumptions about whether the ultimate responsibility for health lies with the individual or with society, and whether the government has a right, or even a responsibility, to regulate individual behaviour and commercial activity to protect and promote the public good. The ideological arguments that most often come into play in discussions of public health policies tend to pit the duty of government to intervene to protect the health of its citizens against the right of individuals to make their own choices.3 Ideological arguments abound in debates about health issues, many of which are not new. Twenty years ago, Beauchamp wrote about the “growing tensions between the goals of protecting the public health and individual liberty”.4 About the same time, Baker described how ideological arguments regarding personal liberty were put forth to oppose mandating the use of motorcycle helmets and had been used for decades to delay milk pasteurisation.5 Arguments against fluoridation of public water supplies span five decades, with a prominent objection being the violation of individual rights.6-8 Of course, arguments in favour of …
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".