An Economic Impact Comparative Analysis of Farmers' Markets in Michigan and Ontario
Bibliographic record
Abstract
Farmers' markets play a vital role in local economic development by providing a site for local and small business incubation, creating an economic multiplier effect to neighboring businesses, and recycling customer dollars within the community. While several studies have evaluated characteristics of farmers' markets within single metropolitan areas, few have compared the impact of multiple markets in socioeconomically contrasting regions.This research compares shopping habits and economic impacts of customers at farmers' markets in two North American cities: Flint, Michigan, and London, Ontario. Overall, 895 market visitors completed surveys. We conducted statistical and spatial analyses to identify differences between these markets. Though geographically proximate and similar in metropolitan size, the two cities differ greatly in recent economic development, social vitality, and public health indicators. The objectives of this article are to quantify the impact that each market has on its local economy and contextualize these impacts in light of the place-specific attributes of each market.Results indicate that customers come from a mix of urban and suburban locations, but that key urban areas do not draw a substantial share of customers. Marketing efforts in nearby disadvantaged neighborhoods, therefore, might yield new customers and increase multiplier effects within the neighborhoods. The London market drew slightly younger customers who shopped less frequently, while the Flint market drew an older crowd that attended more regularly. This may be attributable to the relative age of the markets, and certainly reflects the marketing push of each market's managers. Given the opportunity to compare similarities and differences, much can be learned from each market in terms of opportunities for marketing, local economic development, and increased community vitality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".