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Record W2244475324

Legal interventions to address obesity: assessing the state of the law in Canada

2011· article· en· W2244475324 on OpenAlexaboutno aff
Nola M. Ries, Barbara von Tigerstrom

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsLawState (computer science)Psychological interventionPolitical scienceMedicineComputer scienceNursing
DOInot available

Abstract

fetched live from OpenAlex

Over the past decade, public health experts have raised the alarm over the expanding number of people in countries around the world who are overweight and obese. Canada is no exception: approximately 60 per cent of Canadian adults and 35 per cent of children are overweight or obese. Obesity, in particular, is associated with higher rates of diabetes, hypertension, cardiovascular disease, and some cancers. The medical, economic, and social consequences of these rates of overweight and obesity, especially among children, have caught the attention of our governments. In the last several years, various provincial and federal committees have produced detailed reports on obesity, which can be stacked alongside similar reports issued by ocher countries, as well as those from international bodies such as the World Health Organization. These reports are uniform in their calls for coordinated and comprehensive measures-including legal interventions-to promote healthier diets and more physical activity. Although a growing body of literature explores law as a tool to control factors associated with obesity, existing analyses focus predominantly on the legal and cultural context of the United States.' Our objective in this article is to provide a systematic analysis of the use of legal interventions to address obesity in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.278
GPT teacher head0.311
Teacher spread0.033 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

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