From greedy grocers to carbon taxes and everything in between: what do we think we know about food prices in Canada and how strong is the evidence?
Bibliographic record
Abstract
In Canada, the task of explaining food prices falls to a handful of grey literature reports that shape media coverage and public understanding and carry significant political and policy influence. We performed an in-depth analysis of 51 of these influential reports, including 39 reports by Statistics Canada (including Consumer Price Index reports and other studies) and 12 reports from the Canada Food Price Report (CFPR) series. Our goal was twofold: 1) to identify and classify the various explanations given for food price changes, and 2) to evaluate the scientific rigor of these explanations. We identified 232 total explanations for food price changes, spread across seven thematic categories and 32 sub-categories. We find that most claims made in these reports are scientifically incomplete (only 28.6% of all claims meet established criteria for the completeness of scientific arguments). We also identify a lack of comprehensiveness in the areas of emphasis and the claims being presented and drivers being explored, particularly with respect to a issues presently at the centre of food price discourse in Canada, such as the agency of grocers and other supply chain actors, corporate growth imperatives, and climate change. Considering the importance of food prices and food security to prosperity and well-being in Canada, we conclude with a series of recommendations for strengthening the scientific rigor of these reports, including greater inclusion of supporting evidence, opportunities for peer review, and increased transparency regarding conflicts of interest and funding sources.
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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 | 0.041 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.030 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".