Successes and Challenges Implementing a “Fresh from the Farm” Fundraising Program in Schools
Bibliographic record
Abstract
In 2013, 4 partner organizations: Dietitians of Canada (DC); Ontario Fruit and Vegetable Growers Association; Ontario Ministry of Agriculture, Food and Rural Affairs; and Ontario Ministry of Education created "Fresh from the Farm" (FFF), a healthy fundraiser for Ontario Schools. FFF was designed to support the Ontario government's School Food and Beverage Policy and Local Food Act and to provide a feasible alternative for less healthy fundraising options. This paper outlines the program successes and challenges over the 6 years of DC's involvement. After 6 years, over 1700 schools successfully participated in FFF and over $2 million has been paid to Ontario farmers for product and distribution. The average participating school has generated $2040 in sales towards their fundraising efforts, equating to 770 kg (1700 lbs) of fresh produce per school. Schools reported high satisfaction with FFF, with over 90% of participating schools enrolling in subsequent years. The main reasons for satisfaction included: easy to implement, profitable, offers a healthy alternative to "traditional" fundraising programs, and provides great value for cost. The main challenges were logistics of sourcing and delivery, higher than anticipated costs that made the financial model less feasible than predicted, and competition from other fundraisers.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".