Leveraging artificial intelligence to monitor unhealthy food and brand marketing to children on digital media
Bibliographic record
Abstract
Food and brand marketing refers to commercial promotions designed to increase recognition, appeal, and consumption of particular foods and brands. 1 WHOA framework for implementing the set of recommendations on the marketing of foods and non-alcoholic beverages to children. https://www.who.int/dietphysicalactivity/framework_marketing_food_to_children/en/Date: 2012 Date accessed: January 1, 2019 Google Scholar Successive systematic reviews 2 Boyland EJ Nolan S Kelly B et al. Advertising as a cue to consume: a systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults. Am J Clin Nutr. 2016; 103: 519-533 Crossref PubMed Scopus (298) Google Scholar , 3 Smith R Kelly B Yeatman H Boyland E Food marketing influences children's attitudes, preferences and consumption: a systematic critical review. Nutrients. 2019; 11: E875 Crossref PubMed Scopus (151) Google Scholar have shown that unhealthy food and brand marketing, particularly on television and within advergames (ie, advertising in video games), adversely affects children's diet quality and diet-related health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".