Content analysis of television food advertisements aimed at adults and children in South Africa
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency and content of food-related television (TV) advertisements shown on South African TV. DESIGN: Four national TV channels were recorded between 15.00 and 21.00 hours (6 h each day, for seven consecutive days, over a 4-week period) to: (i) determine the number of food-related TV advertisements; and (ii) evaluate the content and approach used by advertisers to market their products. The data were viewed by two of the researchers and coded according to time slots, food categories, food products, health claims and presentation. RESULTS: Of the 1512 recorded TV advertisements, 665 (44 %) were related to food. Of these, 63 % were for food products, 21 % for alcohol, 2 % for multivitamins, 1 % for slimming products and 13 % for supermarket and pharmacy promotions. Nearly 50 % of food advertisements appeared during family viewing time. During this time the most frequent advertisements were for desserts and sweets, fast foods, hot beverages, starchy foods and sweetened drinks. The majority of the alcohol advertisements (ninety-three advertisements, 67 %) fell within the children and family viewing periods and were endorsed by celebrities. Health claims were made in 11 % of the advertisements. The most frequently used benefits claimed were ‘enhances well-being’, ‘improves performance’, ‘boosts energy’, ‘strengthens the immune system’ and ‘is nutritionally balanced’. CONCLUSIONS: The majority of food advertisements shown to both children and adults do not foster good health despite the health claims made. The fact that alcohol advertisements are shown during times when children watch TV needs to be addressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".