Food advertising on Argentinean television: are ultra-processed foods in the lead?
Bibliographic record
Abstract
OBJECTIVE: To describe the number of processed and ultra-processed food (PUPF) advertisements (ads) targeted to children on Argentinean television (TV), to analyse the advertising techniques used and the nutritional quality of the foods advertised, and to determine the potential exposure of children to unhealthy food advertising in our country. DESIGN: Five free-to-air channels and the three most popular children's cable networks were recorded from 07.00 to 22.00 hours for 6 weeks. Ads were classified by target audience, type of product, advertised food categories and advertising strategies used. The NOVA system was used to classify food products according to industrial food processing level. Nutritional quality was analysed using the Pan American Health Organization's nutrient profile model. SETTING: Buenos Aires, Argentina. Results are considered applicable to most of the country. SUBJECTS: The study did not involve human subjects. RESULTS: Of the sample of food ads, PUPF products were more frequently advertised during children's programmes (98·9 %) v. programmes targeted to the general audience (93·7 %, χ 2=45·92, P<0·01). The top five food categories were desserts, dairy products, non-alcoholic sugary beverages, fast-food restaurants, and salty snacks. Special promotions and the appearance of cartoon characters were much more frequent in ads targeting children. Argentinean children are estimated to be exposed to sixty-one ads for unhealthy PUPF products per week. CONCLUSIONS: Our study showed that Argentinean children are exposed to a high number of unhealthy PUPF ads on TV. The Argentinean Government should build on this information to design and implement a comprehensive policy to reduce exposure to unhealthy food marketing that includes TV and other communication channels and places.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".