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Record W2018494930 · doi:10.1057/jphp.2013.9

Restricting marketing to children: Consensus on policy interventions to address obesity

2013· article· en· W2018494930 on OpenAlexafffundabout
Kim D. Raine, Tim Lobstein, Jane Landon, Monique Potvin Kent, Suzie Pellerin, Timothy Caulfield, Diane T. Finegood, Lyne Mongeau, Neil Neary, John C. Spence

Bibliographic record

VenueJournal of Public Health Policy · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsInstitute of Population and Public HealthUniversity of OttawaUniversité de MontréalSimon Fraser UniversityUniversity of Alberta
FundersUniversity of AlbertaHeart and Stroke Foundation of Canada
KeywordsPublic health lawPublic healthFood marketingHealth policyEnvironmental healthPsychological interventionBusinessObesitySocial marketingMarketingPublic health policyCompliance (psychology)Public policyPolitical sciencePublic relationsMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

Obesity presents major challenges for public health and the evidence is strong. Lessons from tobacco control indicate a need for changing the policy and environments to make healthy choices easier and to create more opportunities for children to achieve healthy weights. In April 2011, the Alberta Policy Coalition for Chronic Disease Prevention convened a consensus conference on environmental determinants of obesity such as marketing of unhealthy foods and beverages to children. We examine the political environment, evidence, issues, and challenges of placing restrictions on marketing of unhealthy foods and beverages within Canada. We recommend a national regulatory system prohibiting commercial marketing of foods and beverages to children and suggest that effective regulations must set minimum standards, monitor compliance, and enact penalties for non-compliance.

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 imitation

Not 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.

metaresearch head score (Codex)0.108
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.108
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1080.121
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0040.014
Scholarly communication0.0120.008
Open science0.0100.009
Research integrity0.0220.033
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.094
GPT teacher head0.397
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations50
Published2013
Admission routes3
Has abstractyes

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