Mapping the Research in Environmental and Sustainability Education in Teacher Education in Canada
Bibliographic record
Abstract
This article examines the evolving landscape of environmental and sustainability education in teacher education (ESE-TE) in Canada through a scoping review of 198 peer-reviewed studies published between 2005 and 2020. Situated within broader international efforts to reorient education toward sustainability, the review maps the theoretical, methodological, and linguistic contours of ESE-TE scholarship across the country. Findings reveal a field characterized predominantly by qualitative research, strong representation from Ontario-based scholars, and limited contributions in French and from northern regions. The increasing integration of Indigenous education and land-based learning signals a promising yet complex engagement with decolonizing approaches to sustainability. However, significant gaps persist in the inclusion of diverse epistemologies, methodological breadth, and Indigenous-led scholarship. By tracing these developments, this study contributes to ongoing conversations about how Canadian faculties of education might mobilize research and collaboration to address the climate crisis and advance the transformative aims of sustainability education. Keywords: environmental and sustainability education, teacher education, scoping review, Indigenous education, climate crisis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.060 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".