Kitchen Wizards: Community Engaged Learning at The Wolfville Farmers’ Market
Bibliographic record
Abstract
This article tells the story of an introductory, undergraduate required course with a significant community service-learning project developed in partnership between the School of Nutrition and Dietetics at Acadia University and the Wolfville Farmers’ Market. This partnership began in 2009, with the vision of putting food and community at the centre of the School’s pedagogy. After two years of developing a trusting relationship between the partners with the integration of focused assignments, a community-service learning initiative called Kitchen Wizards was created. Kitchen Wizards, now in its 10th year, engages 50 to 80 first-year School of Nutrition and Dietetics’ students with the community each fall semester through a Food Commodities course. The initiative introduces 6 to 12-year-old children to in-season local vegetables through a taste-testing experience centered around a simple, healthy recipe made from local produce at the Farmer’s Market, which gives the children purchasing power to buy a vegetable with a three-dollar voucher after participating in the tasting. This Kitchen Wizard’s story was developed from an action research case study, grounded in a constructivist paradigm, which explored the community-valued outcomes of this program over a three-year period, as well as the student and institutional benefits. This study was conducted by a team that included the Wolfville Farmers’ Market Coordinator and the Director of the School of Nutrition and Dietetics who teaches the Food Commodities course. Through observation, dialogue and in-depth interviews conducted with students, teaching assistants, community members, Market staff, faculty, and university administration, insights were derived that illuminate community engaged learning as a key strategy for teaching about local food systems that puts both food and community at the centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".