Exploring price changes in local food systems compared to mainstream grocery retail in Canada during an era of ‘greedflation’
Bibliographic record
Abstract
• More data is needed to understand food price dynamics across Canadian food systems, comparing mainstream and local markets. • The study compares food price trends in local and mainstream systems using mixed methods and five years of price data. • Research includes price data analysis over five years and vendor interviews to explore pricing trends in food systems. • Farmers’ markets showed smaller price increases than grocery stores, despite facing similar rising input costs. • Farmers’ markets had flat or declining margins, while grocery stores saw rising margins despite rising costs. In the wake of the COVID-19 pandemic, rising food prices have become a defining feature of the global landscape. In high-income countries, rising food prices have been accompanied by record corporate profits, sparking allegations of “greedflation”. Policymakers around the world are investigating ways to curb rising food prices and build more sustainable food systems. Strikingly missing from this policy conversation is the role of diverse, local alternatives, like farmers’ markets in supporting more resilient food systems. This study investigates the inflationary dynamics within Canada’s local food systems compared to mainstream grocery retail. Employing a mixed methods approach, the research team analyzed price data from 223 farmers’ market vendors across Canada from 2018 to 2023 and conducted 17 semi-structured interviews with vendors. The exploratory findings reveal that most local food products experienced less inflation than those in mainstream grocery stores. The results underscore the need for policy frameworks that support local food systems to enhance food security and sustainability. The study contributes to the broader discourse on food price inflation and corporate concentration, offering insights that are relevant beyond the Canadian policy context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".