Defining child-directed websites: Implications for limits on food advertising to children through the Children’s Food and Beverage Advertising Initiative (CFBAI)
Bibliographic record
Abstract
Companies that participate in the CFBAI have pledged that they will only advertise healthier dietary choices on child-directed third-party websites.1 The majority of companies define child-directed websites as those where 30 % or 35 % or more of total visitors are children under age 12.2 This report examines children’s entertainment websites to determine whether they would qualify as child-directed media according to CFBAI participants. Methods To identify child-directed third-party websites that would be covered by food companies ’ CFBAI pledges, we analyzed exposure data for all websites classified by comScore as “Entertainment- Kids ” websites. comScore designates a website as kids ’ entertainment if the content includes activities and online games for kids, based on examination of the sites by their dictionary team. We used the comScore Media Metrix Key Measures Report to obtain the number of average monthly unique visitors to these websites during the first two quarters of 2012 for the following age groups: 2-11 years, 12-14 years, 2-17 years, and 2+ years.3 To obtain the number of unique visitors in the 2-14 years age group, we added the numbers of unique visitors among youth 2-11 and 12-14 years. The report also provided unique visitors to all kids ’ entertainment websites and the total internet during the same time periods. We analyzed all websites on the comScore Entertainment – Kids report that had at least 100,000 unique child visitors during either the first or second quarter of 2012. In most cases, we examined data for specific URLs (e.g., Nick.com,
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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 | 0.067 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".