Climate change and educational research: Mapping resistances and futurities
Bibliographic record
Abstract
Given that human-caused climate change is one of the defining educational contexts in the 21st Century, we ask this question of ourselves and our educational research community: What is the role of education and educational research as we attempt to “cultivate equitable educational systems” in a world dominated by climate breakdown and related emergencies? We suggest our scholarly community needs to examine the systems and ideologies that are responsible for climate change: human supremacy, colonialism, capitalism, industrialization, and white supremacy, among others. The perpetuation of these ideas via educational institutions and practices is a significant part of the problem that has led to the current climate crisis. Therefore, the aim of this special issue of Research in Education is to draw together scholarship that can help map out potential roles of education in both the possibilities and resistances of addressing climate change. Collectively the papers map possible and much-needed educational futures where climate change is a matter of urgent superordinate concern including through enacting resistance to human-centrism, coloniality, racial capitalism, and their interconnections. In these futures, climate change education inquires - at multiple scales - into possibilities for materializing less extractive and more livable worlds through education policy and data infrastructures to youth coalitions and even the small everyday encounters with the more-than-human world. The papers also illustrate the potentials of climate change pedagogical orientations that are affective, interdisciplinary and intergenerational. We hope this special issue prompts our colleagues to consider how the collective work of educational scholarship might produce desirable futures amid a rapidly changing climate.
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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 | 0.078 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.031 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.041 | 0.065 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.008 | 0.013 |
| 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".