An ethical imperative for higher education: cultivating community partnerships in climate change teacher education
Bibliographic record
Abstract
Stronger links between communities and education systems hold potential to better address the justice-related issues surrounding climate change. Engaging local communities—especially those most impacted by climate change—deepens the understanding of both teachers and students, broadens their perspectives, and fosters a collective sense of responsibility. Drawing on a social cartography with educators, students, and community members in post-industrial Cape Breton, this article maps possibilities for teacher education at the intersection of climate change and social justice, understanding the role of higher education as a cultural catalyst with an ethical imperative to care for and actively support the sustainable well-being of communities. Findings from this research speak to the possibilities for community-grounded teacher education, which might respond to local realities, centre local knowledges as foundational rather than supplemental, and reorient climate justice education around context, pedagogy, and ethical orientation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".