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Record W2137038449 · doi:10.1089/chi.2012.0013

Marketing Foods to Children: Are We Asking the Right Questions?

2012· article· en· W2137038449 on OpenAlexafffund
Charlene Elliott

Bibliographic record

VenueChildhood Obesity · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchHealth CanadaCanada Research Chairs
KeywordsMarketingSocial marketingPsychologyMedicineAdvertisingBusinessInternet privacyComputer science

Abstract

fetched live from OpenAlex

The childhood obesity epidemic has prompted a range of regulatory initiatives that seek to reduce the impact of food marketing on children. Policy recommendations by government and public health organizations have suggested regulating the promotion of high-sugar, -fat, and/or -salt foods to children, while the food industry has created voluntary nutrition guidelines to channel child-targeted marketing toward only "better-for-you" products. This article argues that the overarching focus on the nutrient profile of foods (nutritionism) is wrong-headed: The slippage in terms from "better-for-you" foods to "healthy dietary choices" is problematic and also makes it difficult for children to identify the healthy choice. Nutritionism further works to sidestep important questions pertaining to the ethics of food marketing, not to mention the way that marketing foods as fun and entertainment works to encourage overeating in children.

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)0.031
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.102
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0070.040
Scholarly communication0.0150.027
Open science0.0020.006
Research integrity0.0160.021
Insufficient payload (model declined to judge)0.0100.005

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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 designTheoretical or conceptual
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

Citations16
Published2012
Admission routes2
Has abstractyes

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