Understanding and developing food pedagogies in Ontario pre-service education
Bibliographic record
Abstract
Policy documents implore Ontario teachers to integrate environmental education (EE) in the curriculum. Evidence of significant barriers such as lack of time, resources and knowledge, and lack of preparation at the Bachelor of Education level to teaching EE is well documented (Barrett, 2007, 2013; Stevenson, 2007; Thompson, 2004). Food literacy (FL) is often considered a framework from which to understand environmental issues, thus the authors sought to consider its’ usefulness in aiding integration of EE curricula. Using a ‘theory into practice’ approach we asked: Can food literacy be used to make environmental issues more relevant and accessible, thus diminishing the barriers to teaching EE? How do pre-service teachers define FL and do they know enough to use this framework? Qualitative interviews were conducted with thirteen Ontario pre-service teachers to determine their understanding of FL. Findings included a lack of exposure to FL concepts, however, there was an interest to using FL to help teach EE. Some suggestions to improve food pedagogy in the pre-service program and placements included: curriculum changes that made explicit connection to food; clear linkages between environmental issues and food; empowering students to do projects, debates and assignments on food, and experiential learning. Ultimately, there was interest and promise of utilizing FL to integrate EE, but a change of culture at the pre-service level is needed for it to be supported.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".