An emerging signature pedagogy for sustainable food systems education
Bibliographic record
Abstract
Abstract Concerns are growing over the ability of the modern food system to simultaneously achieve food security and environmental sustainability in the face of global change. Yet, the dominant tendency within university settings to conceptualize and address diverse food system challenges as separate, disconnected issues is a key barrier to food system transformation. To address this fragmented approach, educators in North American institutes of higher education have begun new degree programs, specializations and certificates related to food systems. These programs, which we term sustainable food system education (SFSE) programs, have a common goal: to support post-secondary students across a range of disciplines in developing the knowledge, skills and dispositions to effectively address complex challenges in the food system. Graduates of these programs will be able to engage in collective action towards transforming the food system. As educators participating in flagship SFSE programs, we identify common pedagogical themes evident in SFSE programs, including our own. We then propose a signature pedagogy (SP) for sustainable food systems education. Signature pedagogies are conceptual models that identify the primary elements by which professional education in a specific field is designed, structured and implemented. On the basis of our analysis of SFSE programs, we identified systems thinking, multi-, inter- and trans-disciplinarity, use of experiential learning approaches and participation in collective action projects as central themes within a SFSE SP. By making these themes and their function explicit within a pedagogical framework, we seek to spur critical and creative thought regarding challenges of professional education in the field of sustainable food systems. Scholars and practitioners are encouraged to review, critique and implement our framework to advance the dialogue on SFSE theory and practice.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 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".