Fruit and Vegetable Purchases in Farmer’s Market Stands: Analysing Survey and Sales Data
Bibliographic record
Abstract
Farmers’ market implementation holds promise for increasing access to healthy foods. Although rarely measured, purchase data constitute an intermediate outcome between food environment and actual consumption. In a study conducted with two seasonal Fruits and Vegetables (FV) stands in a disadvantaged area of Montréal (Canada), we analysed how accessibility, perception, and mobility-related factors were associated with FV purchase. This analysis uses a novel measure of FV purchasing practices based on sales data obtained from a mobile application. A 2016 survey collected information on markets’ physical access, perceived access to FV in the neighbourhood, usual FV consumption and purchases. Multivariate models were used to analyse three purchasing practice indicators: number of FV portions, FV variety and expenditures. Average shoppers purchased 12 FV portions of three distinct varieties and spent 5$. Shoppers stopping at the market on their usual travel route spent less (p = 0.11), bought fewer portions (p = 0.03) and a lesser FV variety (p < 0.01). FV stands may complement FV dietary intake. Individuals for whom the market is on their usual travel route might make more frequent visits and, therefore, smaller purchases. The novel data collection method allowed analysis of multiple purchase variables, is precise and easy to apply at unconventional points of sales and could be transposed elsewhere.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".