Kids Growing: Implementing School-Community Gardens in Ontario
Bibliographic record
Abstract
Cultivating land surrounding schools provides opportunities for children and youth to experience growing, tasting and preparing fresh food. This creates openings for deeper understanding of environmental and social sustainability and can transform early learning. The literature supports evidence of a variety of social and educational benefits of school gardens. School gardens are recommended in policy frameworks but are not actually supported in practice in the province of Ontario, Canada. This paper reviews gaps between policy and practice. Using Social Cognitive Theory, this paper contributes to the discussion of benefits to students and to teachers, and barriers to implementation of school gardens. It is situated in wider discussions about food literacy and environmental literacy in school-based interventions. In the comparative case study, teachers in two schools discuss the benefits of school gardens and barriers relating to implementation. Teachersâ attitudes are compared with the literature. Teachers in one of the two schools have the assistance of a community-based non-profit partner helping to create, maintain and support teaching in the school garden. Teacher attitudes towards policy and practice in each school are examined in a collaborative inquiry, with the researcher as participant through founding the community-based group. The paper concludes that school food gardens can be pivotal to the practice of a rich, multilayered and transformative pedagogy in the face of climate change, economic polarization and urbanization. If education for sustainability is to have more traction in Ontario, the means must be fostered by a more universal and intentional approach from a young age. School gardens present an opportunity to realize benefits for the whole community across intersecting indicators: health, including physical and mental health, sustainability education and authentic academic learning. However, to more adequately and equitably realize their benefits, efforts to bridge gaps in training and resources must be stepped up. This paper intends to support policymaking regarding school gardens, by examining the conditions required for success. Recommendations for pathways to implementation are included in the Efficiency-Substitution-Redesign matrix.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".