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 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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".