Exploring how University and School Board Partnerships can integrate Professional Development in Environmental and Sustainability Education
Bibliographic record
Abstract
Every level of education must support societal transformation towards environmental sustainability; with over eight million students, teachers and professors in Canada, both preservice and in-service K-12 teacher education is a key area that can contribute to addressing the climate crisis through Environmental & Sustainability Education (ESE). Yet these approaches to professional learning have often been delivered in isolation, with little precedence in bringing both preservice and inservice teachers together for integrated ESE professional learning opportunities. This article shares an exploration of this type of integrative professional development by introducing an innovative collaboration between a preservice teacher training program in one of Canada’s largest faculties of education and a school board with one of the country’s most active EcoSchools programs. It also presents the initial findings of a three year case study that tracks the impacts of this partnership in professional development. With some early successes already being identified, this partnership may prove to be an innovative addition to the literature on how university/school board partnerships can support professional learning in ESE.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".