Cultivating a Jewish Eco- Education Framework: The Toronto Heschel School’s Teaching and Learning Garden
Bibliographic record
Abstract
Over the last century, global food systems have increasingly shifted towards a scientific, input-based industrial paradigm whose adverse ecological impacts are well documented. In response, global agricultural movements, such as the agroecology movement, have sought an integration of contemporary science and indigenous agricultural knowledges. Scholars in the field of Jewish ecology similarly propose that biblical Hebrew scripture may provide a framework for thinking about, and acting upon, issues of ecological sustainability in agriculture. In educational settings too, learning about ecology is increasingly approached in an intimate and tangible way in the form of school garden programs. In this work, we highlight the case of the Toronto Heschel School, a Canadian Jewish day school for children in which a Teaching and Learning Garden fosters student learning on ecology and their Jewish identity in tandem. Reporting the thematic results of qualitative interviews with two educators and five alumni from the Heschel School, this study highlights key similarities and differences between this school’s garden program and those taking place in secular school settings. Like secular school gardens, the Heschel program implements experiential, interdisciplinary learning activities within a scientific ecological knowledge (SEK) framework. That said, the Heschel program supplements and deepens these SEK activities with traditional ecological knowledge (TEK) rooted in Judaic traditional teachings. The resulting educational outcomes appear at once universally applicable while specifically relevant to the Jewish identities of learners.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 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".