The Impact of Front-of-Package Label Design on Consumer Understanding of Nutrient Amounts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A between-groups experiment examined the salience of front-of-package (FOP) symbols. Adults from Canada, the US, Australia, and the UK completed an online survey (n = 11,617). Respondents were randomized to view cereal boxes displaying one of 11 FOP label conditions for ‘high’ levels of sugar and saturated fat: control (no FOP symbol), red circle, red ‘stop sign’, magnifying glass, magnifying glass + exclamation mark, and ‘caution’ triangle + exclamation mark, plus each of these five conditions accompanied by a ‘high in’ text descriptor. Participants identified the amount of saturated fat and sugar in the product (‘low’/’moderate’/’high’). Participants were more likely to correctly identify the product as ‘high’ in saturated fat or sugar when shown the stop sign, triangle + exclamation mark, red circle, or magnifying glass + exclamation mark symbols incorporating ‘high in’ text (p < 0.01). The magnifying glass was the least effective symbol. The stop sign (37.7%) and triangle + exclamation mark (22.0%) were most frequently selected as the best symbol for indicating high nutrient amounts. Overall, FOP labels with ‘high in’ descriptions, red color and intuitive ‘warning’ symbols (e.g., stop signs, exclamation marks, ‘caution’ triangles) were more effective at communicating high levels of nutrients of public health concern in a time-limited environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it