Mapping Climate Change Education: Reflections from an Education Design-Based Research Project from Northern British Columbia, Canada
Bibliographic record
Abstract
Climate change education poses significant challenges for K-12 teachers in northern British Columbia (B.C.) due to the complexity of the challenges of climate change and limited support for integration into teaching practice. In response to the 2022 Accord on Education for a Sustainable Future, the Climate Education in Teacher Education (CETE) project was initiated at the University of Northern British Columbia (UNBC). CETE aims to address an urgent need to respond to the changing climate of northern B.C., where warming is occurring 2-3 times faster than in southern B.C., by equipping teachers with practical and tailored approaches and supports. Using the methodology of Education Design-Based Research, CETE employs the conjecture mapping method to develop educational interventions and refine trajectories to be responsive to local realities. Over three years (2022-2025), CETE has engaged in strategic collaborations with climate change education experts, resulting in the generation of seven high level conjectures that guide research and action. This paper examines patterns around roles of community cooperation and iterative design, through action research aimed towards empowering northern B.C. teachers and researchers to respond climate change education demands by considering agency, creative pedagogy, educational leadership, and climate change adaptation, in hopeful and intentional ways.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.057 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".