Utilizing Collaborative Self-Study to Explore Pedagogies for Sustainability
Bibliographic record
Abstract
Abstract Whole school approaches (Wals and Mathie, Whole school approaches to sustainability: Exemplary practices from around the world. Wageningen University, Education and Learning Sciences, 2022) to sustainability where various facets or currents of environmental education (e.g., Sauvé, Can J Environ Educ 10(1): 11–37, 2005) are seamlessly integrated with disciplinary subject matter, leadership practices, and everyday classroom routines remain elusive in many contexts as they serve as counter-narratives to the status quo promoting over-consumption and exploitation of environments and people. We present a case study exploring how a Canadian post-secondary institution, Cape Breton University, has enacted inclusive (e.g., Indigenous perspectives), whole school approaches to sustainability in novel and immersive ways. We present this work as a reflexive, collaborative self-study examining program objectives in teacher education and how these sustainability goals have been interpreted, translated, and implemented at the course level in pre-service teacher education (i.e., science methods courses) and at the graduate level (i.e., an applied research project course for the Master of Education in Sustainability, Creativity, and Innovation).
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| 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".