FoodUCation
Bibliographic record
Abstract
Community engaged learning involves using knowledge to provide solutions to community needs and is widely integrated in higher education across North America.These experiences connect community service with academic study, and the reported benefits include enhanced academic learning, promotion of skills and knowledge needed for leadership, an increased sense of civic responsibility in students, and development of an inquiring mind and imagination.Community engaged learning may be of particular interest in graduate level studies, as students are focused on the development of skills that will be of value in their chosen vocations.In this paper, we describe the development, activities, and impact of a community engaged project called FoodUCation from the graduate student perspective.The mission of the FoodUCation program is "to promote a novel approach to healthy eating known as 'lifestyle medicine', which focuses on food consumption for optimizing health and performance".The FoodUCation project was developed as part of a one-semester graduate course and was piloted in a local elementary school community partner.Student responses to the FoodUCation project were very positive, and resulted in extension of the project beyond the course in which it was developed.Overall, the experience of the graduate students was highly positive, and demonstrates that graduate level community engaged learning creates unique opportunities for students to learn and develop relationships and skills that have tangible benefits.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.190 | 0.043 |
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".