Promotion of ultra-processed foods in Brazil: combined use of claims and promotional features on packaging
Bibliographic record
Abstract
OBJECTIVE: To assess the availability of different promotional strategies applied for UPF sales in Brazilian food retailers. METHODS: Information available on food packaging was gathered from all packaged products sold in the five largest food retail chains in Brazil in 2017. UPF were identified using the NOVA food classification system. From this sample, data related to promotional characteristics, nutrition claims and health claims were collected and coded using the INFORMAS methodology. Additional claims referring to the Brazilian Dietary Guidelines were also collected. RESULTS: This study evaluated the packaging of 2,238 UPF, of which 59.8% presented at least one promotional strategy. Almost one third denoted a simultaneous use of different promotional strategies in the same packaging. Nutrition claims were the most commonly found promotional strategy, followed by health claims and the use of characters. The food subgroups comprising the highest prevalence of promotional strategies on their labels were: noncaloric sweeteners (100.0%), breakfast cereals and granola bars (96.2%), juices, nectars and fruit-flavoured drinks (92.9%), other unsweetened beverages (92.9%), and other sweetened beverages (92.6%). CONCLUSIONS: Considering the poor nutritional quality of UPF, the widespread presence of promotional features on their packaging highlights the need for marketing restrictions on this kind of product.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".