Misguidance in diabetes nutrition: Food labeling and agency recommendations
Bibliographic record
Abstract
Walking through the grocery store, perhaps hungry after a workout or a busy day, Canadians are bombarded by snazzy food marketing. "Low-Fat", "High in Fibre", "Zero Trans Fat", "Ancient Grains!", the bright words jump out as we navigate the store trying to make choices that are both good for our bodies and enticing for our appetites. Food labels throughout grocery stores broadcast conflicting and one-sided messages about the health appeal of their respective products: advertising often boldly proclaims the "healthy" aspects of products, while marginalizing those aspects that are less healthy. Packaging for whole-wheat crackers, for example, might boast "high in fibre", while the equally important health-related reality of the crackers' high sodium content is only subtly revealed in the requisite Nutritional Information fine print. For consumers, choosing foods that will fuel us appropriately and keep us healthy is not a new problem, but the variety of food products becoming available to us, and their prolific marketing is an overwhelming factor in the diabetes epidemic in Canada.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".