From tensions to transformation: Teaching food systems in a graduate dietetics course
Bibliographic record
Abstract
Dietitians are deeply embedded within food systems, so food systems concepts are becoming an essential component of dietetic education in Canada. Yet how can we, as educators, better prepare future dietitians to embrace the complexity of food systems and be forces of change towards equity? In an effort to explore this question in a practical way, we integrated food systems concepts into a mandatory course of a public health graduate dietetics program. This field report shares our experiences teaching food systems over five years based on our notes kept, student feedback, and course evaluations. Our learnings have been in three key areas: intentions, facilitation, and tensions. We recognized that teaching about food systems is value-laden. Hence we have been explicit with the students about our positionality and our intentions in designing the course, partly to meet the management of food systems competency requirements, but also to stimulate thinking about alternative options for purpose, structures, and processes in food systems. Our facilitation approaches aimed to foster a critical consciousness towards social justice and systems change. Using teaching and evaluation methods such as experiential learning, community projects, and reflection assignments, students have encountered the complexity of food systems and the challenges-opportunities they pose. As educators, we have grappled with the tensions of challenging dominant positivist discourses in public health nutrition. Politicized topics such as migrant farm-worker regimes, industrial food production, regulation of food marketing, and mitigation of the impact of colonization have generated debates in the classroom about the role and scope of dietetic practice. Most students have situated themselves more explicitly within a food system, and some began to question hidden structures of power. While it remains challenging to address this breadth within the constraints of one course, we believe it worthwhile to model and stimulate critical reflexivity with the next generation of dietitians as critical food learners-teachers themselves. Even though the course is no longer offered using this food systems approach, course components can be integrated throughout the dietetic curriculum.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".