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Record W1541029970 · doi:10.1002/oby.20826

Changes in the volume, power and nutritional quality of foods marketed to children on television in Canada

2014· article· en· W1541029970 on OpenAlexafffundabout
Monique Potvin Kent, Cherie L. Martin, Emily Kent

Bibliographic record

VenueObesity · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsCarleton UniversityGlobal Affairs CanadaUniversity of Ottawa
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsAdvertisingQuality (philosophy)Food marketingUnhealthy foodMedicineMarketingSpecialtyEnvironmental healthBusinessFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the self-regulatory Children's Food and Beverage Advertising Initiative pre- and post-implementation in terms of volume of marketing, marketing techniques, and nutritional quality of foods marketed to children on television. METHODS: Data for 11 food categories for May 2006 and 2011 were purchased from Nielsen Media Research for two children's specialty channels in Toronto. A content analysis of food advertisements examining the volume and marketing techniques was undertaken. Nutritional information on each advertisement was collected and comparisons were made between 2006 and 2011. RESULTS: The volume of ads aired by Canadian Children's Food and Beverage Advertising Initiative (CAI) companies on children's specialty channels decreased by 24% between 2006 and 2011; however, children and teens were targeted significantly more, and spokes-characters and licensed characters were used more frequently in 2011 compared to 2006. The overall nutritional quality of CAI advertisements remains unchanged between 2006 and 2011. CONCLUSION: There are clear weaknesses in the self-regulatory system in Canada. Food advertising needs to be regulated to protect the health of Canadian 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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.

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

Citations35
Published2014
Admission routes3
Has abstractyes

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