Bibliographic record
Abstract
School garden programs (SGPs) 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, there is little-to-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. Ten semi-structured, open-ended qualitative interviews were conducted in person or by video platform with teachers and community members who acted as school garden program facilitators in south eastern Ontario. Data analysis shows that SGP facilitators had 4 key motivations for implementing SGPs. These include promoting a connection to nature, fostering values of environmental awareness and stewardship, increasing food literacy skills, and introducing students to broader food system issues of inequity and social justice. The major challenges and opportunities included funding, administrative and operational supports (or lack of), partnerships, and long-term visions. The results point to the need for consistent policies, sustained and reliable funding, and other supports from the Ministry of Education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".