The Local Food Paradox: A Second Study of Local Food Affordability in Canada
Bibliographic record
Abstract
The price of food has been affected in recent months in response to events such as the war in Ukraine, energy costs, inflation, the COVID-19 pandemic, and climate change. Indeed, supply problems, as well as the repercussions of global issues, have had an impact on grocery bills. Just between September and October 2022, the price of food increased by 11.4% and 11% year-to-year. In addition, with the pandemic, buying locally has become a key factor for some in choosing which products to consume. This second edition of the report aims to answer the question Does eating local in Quebec cost more? More precisely, our objective was to identify if local products in the food sector, especially in Quebec, were competitive in their price points compared to foods coming from other regions of the world. To answer this, we used AI and machine learning to harvest data from 99 local products and 335 comparable nonlocal products, totaling 198,990 data points and 3745 prices across six data collection dates. The results showed that a total of 25 categories displayed an advantage for the local product or a neutrality, out of a total of 45 categories identified. Our results demonstrated that 55.6% of the categories that contained the local foods analyzed were price competitive with comparable products or less expensive than them.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".