Wolfville local food bucks: A farmers' market coupon program designed to increase community food security
Bibliographic record
Abstract
Millions of people go hungry in Canada each day, and this issue has been addressed by food banks, community food centres, and other models. An alternative model is coupon programs where low-income families receive weekly vouchers for their local farmers' market to purchase more fresh foods. There is research to support the effectiveness of coupon programs in the United States, but limited information pertaining to Canada. Prior to conducting this study, there were no records of any farmers' market coupon programs in Atlantic Canada. The purpose of this research was to explore how best to design a farmers' market food coupon program to increase the access of fresh local food for people with low incomes in rural areas. This was done through a six-week pilot project in Wolfville, Nova Scotia where 17 people received weekly farmers' market coupons. Participants were identified by the local bank and were involved in the design and evaluation of the program through pre- and post-interviews. The program helped overcome the barriers of price, transportation, and seasonality and people's produce consumption increased from 2.12 to 2.940 servings of fruits and vegetables during the program. There were no restrictions on the coupons and 91% were spent on food. Overall, this program was successful and it also helped increase participants' social support networks. Local food must be affordable and accessible to achieve community food security. The lessons learned from this pilot project can provide insight to future planning for programs of this nature in other rural communities in Atlantic Canada and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".