Too Fat for Society? William Bogart and 'Regulating Obesity? Government, Society and Questions of Health'
Bibliographic record
Abstract
Law has a long history of regulating vices, however perceived or defined. Regulation of gambling, consumption of alcohol, and smoking are some examples of vices where recourse to legal regulation has and continues to happen. Obesity, fatness, and external physical appearance of persons is a that is increasingly under social and regulatory scrutiny. Professor Bogart looks at the role and effectiveness of law in promoting, encouraging, and achieving positive health outcomes for individuals. In doing so, he tackles the simple but improper and ineffective foci of regulation - individuals body weight, body size, and outward appearance. The lessons and insights drawn from this study are informative for attempts to regulate other areas of complex human action. In regulating a vice, we must be conscious of the way that the vice is defined and categorized. The shaping of the problem affects how the law may be employed in addressing the problem. Additionally, honesty about the propriety, limits, effectiveness, and unintended-consequences of the use of law must take a central position in the discussion. Bogart's approach in Regulating Obesity serves as a guide for legal scholars who engage with Law's role in addressing important and complex social and human problems. Real human lives and actual human experiences must take center-stage if legislators and legal scholars are to avoid the twin evils of common-sense reaction and ineffective/overzealous governmental interfere though law.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| 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".