Enhancing Food And Nutrition Curricula In Higher Education By Assigning Collaborative Food System Assessment Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Student engagement in higher education is important. Some professional healthcare programs, however, can become quite focused and competitive, limiting the potential for positive student engagement and for students to see how their field of study fits within larger systems. Food system assessments are an ideal way to see the interconnectedness of all parts of a food cycle for a city or region. This case study describes food system assessments conducted by 165 undergraduate students in their first year of a Food and Nutritional Sciences program. Using collaborative, problem-based learning and a photovoice approach, the goal was to help students appreciate the entire food cycle, not just the consumption aspect that dominates much of nutrition education and practice. Students gleaned information about food production, processing, distribution, and waste from their site visits. They also calculated the food miles and CO2 emissions for two foods purchased in their assigned neighborhood. With their final reports, students submitted electronic versions of photographs, which were viewed and discussed during in-class focus groups. The potential for home/community food production prompted the most discussion. While logistics and collaborative learning presented some challenges, this participatory and reflective learning experience promoted positive student engagement among students in higher education. Educators in other university programs may consider enhancing their curricula by assigning collaborative food system assessment projects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it