Food adverts on children's programs on TV in South Africa
Bibliographic record
Abstract
Research in the U.S.A. since the early 1990s has shown that adverts that appear on children's programs on TV are the antithesis of the recommended diet. They are mainly for fast foods and for foods rich in sugar and fat. There is almost no promotion of healthy food choices. Essentially all such previous research has been conducted in developed countries. In this study we examined food advertising on children's TV in South Africa. We recorded 2 sets of children's programs during weekdays: (1) 12 hours of programs (SABC2; 9 am–11 am); these are for children below school age, are mainly in English and Africaan, plus a small amount in Xhosa. Out of 47 ads none were for food. (2) 37.5 hours of programs were recorded on YoTV (SABC1; 3 pm–530 pm). This program is for children aged over approximately 7 years. It is mainly in English plus a small amount in Zulu. Out of 408 ads 69 (16.9%) were for food. Virtually all (97%) of the food ads fall into 2 groups: (1) 38 ads (55%) were for foods of generally poor nutritional value (fast food restaurants, highly refined breakfast cereals, candies, potato chips, and sugar‐rich cold drinks); (2) 29 ads (42%) were for foods of generally good nutritional value (yoghurt and peanut butter). These findings suggest that food ads on children's TV in South Africa is more evenly balance towards healthier foods than is the case in the U.S.A. Further investigation is required to form a clearer picture.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".