Foods and beverages marketed in major food retailer flyers : a repeat cross-sectional study in the Canadian food environment
Bibliographic record
Abstract
Grocery flyers are an important component of the informational food environment that \ncan affect food purchasing behaviour. This thesis used a repeat cross-sectional design to examine \nthe food group content and nutrient profiles of foods and beverages marketed in Canada’s largest \nfood retailer’s flyers in 2014, 2017, and 2021. The largest proportions of flyer items were baked \nproducts (11%), non-alcoholic beverages (10.7%) and milk and dairy (10.2%). Least \nhealthy/ultra-processed items, as coded by three nutrient profiling systems, made up a substantial \nproportion of flyer content (Canada’s Food Guide (CFG): 58.5%, Ontario’s Policy/Program \nMemorandum no. 150 (PPM150): 45.3%, NOVA (not an acronym) Level of Processing \nclassification: 51.2%); however, the healthiest/un/minimally processed groups of items followed \n(CFG: 33.6%, PPM150: 36.6%, NOVA: 27.8%). The only feature of the flyer that consistently \npredicted healthiness was a food being on the first page of the flyer, and not store type, \ngeographic region, or publication year.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".