Brazilian Front‐of‐Package Labeling: A Choice‐Based Conjoint and Eye‐Tracking Study on the Role of the Magnifying Glass Symbol Versus All‐Text Warnings
Bibliographic record
Abstract
ABSTRACT Front‐of‐package labeling (FoPL) systems often use text and visuals to help communicate information about nutrients potentially linked to chronic diseases. While systems like the European Nutri‐Score and the Latin black octagon emphasize clear warnings, the magnifying glass, adopted in Brazil and Canada, lacks clarity in its semiotic interpretation, warranting further study. This research conducted two experiments to assess the magnifying glass's impact on consumer choices: one with eye‐tracking ( n = 30) and another without ( n = 408). Fot this, mock packages of dulce de leche w ere developed for the study. These packages featured statements such as “High in added sugar,” “High in saturated fat,” and “High in added sugar and saturated fat,” presented with or without the magnifying glass symbol. Results showed that combining “High in (…)” warnings with the magnifying glass had a weaker effect on reducing product choice than text‐only labels. Additionally, dual‐nutrient warnings (sugar and saturated fat) consistently had a stronger negative effect on choices than single‐nutrient warnings, regardless of the symbol.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".