Exploring Ecojustice & Environmental Learning through Online Preservice Teacher Education
Bibliographic record
Abstract
As teacher education can help communities transition to sustainable, just ways of being, this study reports on the development of, and research on, a national E-course for preservice teachers focused on Environmental and Sustainability Education (ESE). This collaborative initiative brought together academics, community educators, and K-12 teachers to offer participatory, locally-relevant online ESE learning. By facilitating learning centred on concepts of indigenization, ecojustice, and place-based learning, the E-course aimed to ensure equitable access to ESE for preservice teachers across Canada. We ask, what are participants’ experiences and the impact of their involvement in this E-course? Using a socio-critical lens, the authors draw on survey data to report on the outcomes of the E-course. We hope that it may serve as a new model for online ESE for preservice teacher education, and more broadly highlight the capacity for building understanding about ecojustice education and community connections through virtual learning spaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".