Towards a transformative climate change education: questions and pedagogies
Bibliographic record
Abstract
This paper approaches climate change as a superordinate concern that should guide a holistic transformation of formal schooling towards integration and sustainability. The call for climate change education (CCE) has been amplified by international organizations and youth protestors alike, united by a shared concern for our planet. By combining CCE with principles of transformative learning (TL), the paper outlines a framework for transformative climate change education (TCCE). If climate change fits the description of a super wicked problem, this is also true of TCCE, which requires the simultaneous transformation of curricula, pedagogies, and assessment systems. The paper argues that the implementation of TCCE faces significant challenges because it disputes the underlying values of our transmissive educational systems. Those challenges are formulated here as a series of questions, which are followed by a discussion of sustainability pedagogies that help learners build capacity for understanding and acting on climate change.
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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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".