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Record W2155036195 · doi:10.2174/1876823701406010065

Consuming More than Just Stories: Does What Children Watch on TV While Eating Influence how Much they Eat?

2014· article· en· W2155036195 on OpenAlexafffund
M. Borghese Michael, P. Traversy Gregory, Montgomery Zachary, Jean‐Philippe Chaput

Bibliographic record

VenueThe Open Obesity Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsEnergy (signal processing)Variety (cybernetics)ObesityPromotion (chess)PsychologyCaloric intakeFood intakeAdvertisingCaloric theoryMedicineDevelopmental psychologyEnvironmental healthComputer scienceBusinessEndocrinologyPolitical science

Abstract

fetched live from OpenAlex

Television (TV) viewing is a risk factor for obesity in children. This is partly due to its promotion of energy intake by distracting from normal satiety signals. Children are exposed to a variety of TV programs which differ in features such as pacing. This is reason to believe that different children's TV shows distract to varying degrees, and can therefore influence energy intake to different extents. We suggest that future research should examine how different TV shows influence energy intake in children. Such research could help identify new ways to protect children from the negative effects of TV viewing on caloric intake and ultimately body weight.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0000.001
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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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