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Record W4282825037 · doi:10.1093/cdn/nzac054.061

What Factors Shape Whether Digital Food Marketing Appeals to Children?

2022· article· en· W4282825037 on OpenAlex
Camilo E. Valderrama, Dana Lee Olstad, Yun Jung Lee, Joon Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCurrent Developments in Nutrition · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAppealFood marketingPsychographicPsychologyLogistic regressionOrdered logitMarketingSocial psychologyMathematicsStatisticsPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Children are exposed to large amounts of unhealthy food marketing on digital media. This marketing contains elements that marketing experts consider child-appealing, such as cartoons, bold colors, and childish font styles. Although these elements capture children’s attention, there is still ambiguity regarding which additional factors also play a role. We aimed to examine the effects of child characteristics (sociodemographic, behavioural, and dietary intake factors) and marketing instance features on whether digital food marketing instances appealed to children. Thirty-nine children from Calgary, Alberta, Canada indicated whether 1660 digital food marketing instances (∼130 per child) appealed to them (‘Is this ad for kids like you’; yes vs. no). Each instance was evaluated by three children. Information on children’s sociodemographic characteristics, screen-related behaviours, and dietary intake was also collected. Agreement between children was measured using the Fleiss’ kappa statistic. Text, labels, objects, and logos extracted from the food marketing instances were combined with the variables collected from the children to fit logistic regression, random forest, conditional inference tree, and gradient boosting models to assess which variables were the most important determinants of child appeal. Agreement between children was low, with an average Fleiss’ kappa of –0.01 (95% CI: –0.09, 0.08). The models indicated that the text contained in the food marketing instances was the most important determinant of child appeal. Besides text, the three most important predictors of child appeal were the household’s highest level of education, children’s vegetable consumption and BMI. The conditional inference tree indicated that children with low consumption of vegetables, high consumption of unhealthy snacks and more time spent using screen devices tended to consider more food marketing instances as child appealing. There is substantial variability among children in which marketing instances appeal to them; even children with similar characteristics disagreed. However, children with poorer dietary intakes reported that more food marketing instances appealed to them. Canadian Institutes of Health Research.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.301
Teacher spread0.268 · 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