Monitoring Food Affordability: Reliability and Validity of an Online Nutritious Food Basket
Bibliographic record
Abstract
Purpose: This study aimed to assess the reliability and validity of an online approach to monitoring food affordability in Ontario using the updated Ontario Nutritious Food Basket (ONFB). Methods: The ONFB was priced online in 12 large multi-chain grocery stores to test intra-/inter-rater reliability using percent agreement and intra-class correlations (ICCs). Then, the ONFB was priced in-store and online in 28 stores to estimate food price differences using paired t-tests and Pearson’s correlation for all (n =1708) and matched items (same product/brand and purchase unit) (n = 1134). Results: Intra-/inter-rater agreement was high (95.4%/81.6%; ICC = 0.972, F = 69.9, p < 0.001). On average, in-store prices were less than $0.02 lower than online prices. There were no significant differences between mean in-store and online prices for all items (t = 0.504 p = 0.614). The mean price was almost perfectly correlated between in-store and online (fully matched: R = 0.993 p < 0.001; all items: R = 0.967 p < 0.001). Online monthly ONFB estimates for a family of four were strongly correlated (R = 0.937 p < 0.001) with estimates calculated using in-store data. Conclusions: Online pricing is a reliable and valid approach to food costing in Ontario that contributes to modernizing the monitoring of food affordability in Canada and abroad.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".