Enhancing Food And Nutrition Curricula In Higher Education By Assigning Collaborative Food System Assessment Projects
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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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".