Online Marketing of Food and Beverages to Children: A Content Analysis
Bibliographic record
Abstract
PURPOSE: The goal was to assess websites sponsored by food and beverage manufacturers that have pledged to market branded food and beverage products to children responsibly, by ratifying the Children's Food and Beverage Advertising Initiative (CFBAI). METHODS: A content analysis was conducted of 24 purposively sampled websites sponsored by 10 companies that promote food and beverage products to children. All are participant members of the CFBAI. RESULTS: Of the 24 websites analyzed, the majority targeted children below age 12 (83%). An array of innovative online marketing techniques, most notably free website membership (63%), leader boards (50%), adver-games (79%), and branded downloadable content (76%), were used to encourage children's engagement with branded food and beverage promotions. CONCLUSIONS: Food and beverage manufacturers are engaging children with dynamic online marketing techniques that challenge regulatory codes governing broadcast media. These techniques may contradict the spirit of the CFBAI. Innovative regulatory guidelines are needed to address modern marketing media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".