Observational study of population level disparities in food costs in 2021 in Canada: A digital national nutritious food basket (dNNFB)
Bibliographic record
Abstract
The aim of this work was to assess the feasibility and effect of applying a nationally representative and highly disaggregated food costing measure across Canada, through the novel application of web-scraping technology to the methods of the National Nutritious Food Basket (NNFB). Further, this study tested the hypothesis that a product-matched digital NNFB (dNNFB) correlates with existing market basket measures and quantified any differences in costs. This was an observational cross-sectional study using web scraped food price data collected in November 2021. Food price data was collected from the majority of Loblaw's banners across Canada, resulting in a final store sample of 751 stores sourced from 11 retail banners. Stores were located across all five Statistics Canada regions, including all provinces and territories with the exception of Nunavut. Store-level dNNFB costs were computed, adjusted by age-sex group, and summarized by geographic region and banner. dNNFB costs were then compared with existing national statistics office estimates (Market Basket Measure thresholds for reference families). dNNFB costs varied widely across the country, with notable differences by regional, store-level, and age-sex group characteristics. When compared to reported national statistics, our estimates exceeded the national market basket measure in every comparison in corresponding sub-national geography across the country, with correlation varying from 0.49 to 0.78 dependent on summary comparator. Digital collection of food price data was a feasible strategy for market basket costing. Our findings suggest we may be routinely underestimating the impact of food inflation for consumers, particularly those restricted to certain food environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".