The efficacy of sugar labeling formats: Implications for labeling policy
Bibliographic record
Abstract
OBJECTIVE: To examine knowledge of sugar recommendations and test the efficacy of formats for labeling total and added sugar on pre-packaged foods. METHODS: Online surveys were conducted among 2008 Canadians aged 16-24. Participants were asked to identify recommended limits for total and added sugar consumption. In Experiment 1, participants were randomized to one of six labeling conditions with varying information for total sugar for a high- or low-sugar product and were asked to identify the relative amount of total sugar in the product. In Experiment 2, participants were randomized to one of three labels with different added sugar formats and were asked if the product contained added sugar and the relative amount of added sugar. RESULTS: Few young people correctly identified recommendations for total sugar (5%) or added sugar (7%). In Experiment 1, those who were shown percent daily value information were more likely to correctly identify the relative amount of total sugar (P < 0.05). In Experiment 2, those shown added sugar information were more likely to correctly identify that the product contained added sugar and the relative amount of added sugar in the product (P < 0.05). CONCLUSIONS: Improved labeling may improve consumer understanding of the amount of sugars in food products.
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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 | 0.123 | 0.374 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".