THE CHALLENGES AND OPPORTUNITIES FOR SCHOOL GARDEN PROGRAMS IN ONTARIO
Bibliographic record
Abstract
School garden programs offer students opportunities to experience and participate in the processes of nature and agriculture through hands-on learning in a wide variety of outdoor settings. Although the value of school gardens has been well documented, my 7-year experience as a school garden facilitator is that there is little or no concrete support for these programs within the public-school system itself, either at the local or the provincial level. Most programs operate through the vision and dedication of community members and organizations and/or the efforts of individual educators. The purpose of this study is to investigate how school garden programs are implemented in a variety of educational settings and to identify the challenges and opportunities that exist within them. For my research I conducted ten semi-structured interviews with teachers, educational assistants and community members who acted as school garden program facilitators. Data from these interviews was coded and analyzed to identify key themes as well as situation specific anecdotes. Findings from my study indicate that there are as many models for school garden programs as there are facilitators. Each program is uniquely adapted to the skills and abilities of the facilitator as well as to the setting in which the program takes place. However specific challenges such as the lack of funding and implementation time were universal to all programs. I hope that this work will provide useful insights for other schools and other districts, both in Ontario and across Canada, as well as contribute to almost the non-existent literature on the implementation of school garden programs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".