Alcohol, Tobacco and Obesity: morality, mortality and the new public health
Bibliographic record
Abstract
Although drinking, smoking and obesity have attracted social and moral condemnation to varying degrees for more than two hundred years, over the past few decades they have come under intense attack from the field of public health as an 'unholy trinity' of lifestyle behaviours with apparently devastating medical, social and economic consequences. Indeed, we appear to be in the midst of an important historical moment in which policies and practices that would have been unthinkable a decade ago (e.g., outdoor smoking bans, incarcerating pregnant women for drinking alcohol, and prohibiting restaurants from serving food to fat people), have become acceptable responses to the 'risks' that alcohol, tobacco and obesity are perceived to pose. Hailing from Canada, Australia, the United Kingdom and the USA, and drawing on examples from all four countries, contributors interrogate the ways in which alcohol, tobacco and fat have come to be constructed as 'problems' requiring intervention and expose the social, cultural and political roots of the current public health obsession with lifestyle. No prior collection has set out to provide an in-depth examination of alcohol, tobacco and obesity through the comparative approach taken in this volume. This book therefore represents an invaluable and timely contribution to critical studies of public health, health inequities, health policy, and the sociology of risk more broadly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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".