Eco-Literacy Development through a Framework for Indigenous and Environmental Educational Leadership
Bibliographic record
Abstract
In response to the call of curriculum reforms at the international, national, and local levels, we conceptualize an eco-mentorship program and envision a learning garden alternative practica. We aim to advance a framework enabling the inno-vation of Indigenous environmental studies, eco-justice education, and Western scientific environmental knowledge as part of a larger eco-literacy and environ-mental leadership curriculum in Ontario. We explore the application of a frame-work for environmental learning that involves eco-hermeneutic practices, making historical and etymological connections between land and language through the development of relationships with place. As gathering and gardening practices have taken place in Indigenous cultures since time immemorial, we investigate how earth, water, food, and plants can be explored through an inter-generational knowledge model. Résumé En réponse au besoin d’actualisation des programmes d’études aux niveaux
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".