Towards an ecology of knowledge for sustainable food system education
Bibliographic record
Abstract
This study investigates the Land, Food, and Community (LFC) series of core courses within the Faculty of Land and Food Systems at the University of British Columbia (Vancouver) as an example of a sustainable food system education (SFSE) program. SFSE programs are emerging across North America as an alternative approach to developing future professionals within the food system. SFSE programs share common curricular use of systems-based, inter- and trans- disciplinary, experiential learning approaches. The study has two components. The first component focuses on the theoretical framework of the LFC series. The second component focuses on experiences of a sample of students enrolled in a second-year course in 2012. Using a qualitative case study methodology, I incorporate autoethnographic experiences, document analysis, semi-structured interviews, and analysis of written responses to a dual-position food security problem to address the following research objectives: identify and situate the LFC theoretical framework within a diversity of scholarly domains; determine the impact of specific pedagogical strategies in the second-year course on students’ epistemic and ontological cognitive development (EOCD); and, investigate the correspondence between EOCD and systems competencies in addressing food systems problems. Results from the first component identify four key elements of the LFC theoretical framework: the ecology of knowledge, the community of learners, polycultures of the mind, and systems approaches. The elements are situated within Freirean critical constructivist learning theory, Bateson’s theory of ecological knowing, cognitive flexibility theory, and community- engaged scholarship. Results from the second component suggest that the pedagogical activities in the second-year course had an impact on participants’ EOCD by helping students understand the concepts of holism and pluralism as well as demonstrating the limitations of reductionism and the uncertainty of knowledge in ill-structured domains. Evidence suggests that systems thinking competencies are not being adequately developed in the second-year course. The study found limited evidence of correspondence between EOCD and the ability to apply systems principles to address a food system issue. The study concludes with a discussion on characteristics of a signature pedagogy for SFSE programs and a proposed scaffolding of EOCD and the development of systems thinking competencies across the LFC series.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".