Awakening Sleepy Knowledge: Extrarational Pedagogies and Transformative Learning in Environmental Justice Education
Bibliographic record
Abstract
Centuries of settler-colonial capitalist hegemony have deeply embedded violent paradigms of separation and hierarchy in our societal structures and internalized ideologies, ultimately manifesting in global climate justice crises. We argue, therefore, that addressing the socio-ecological catastrophes we currently face necessitates an inclusive engagement with a diversity of ways of knowing often overlooked or actively repressed by dominant epistemologies. Dismantling oppressive structures begins with critically examining how they manifest within our habits of mind so we can then see beyond them—to capture what was lost, what is hidden, and what could be. Drawing on a diversity of theories, this largely conceptual paper calls for the (re)introduction of pedagogies that engage our creativity and extrarational senses to help transform our perspectives toward environmental justice. Extrarational pedagogies provide space to challenge dominant paradigms, empathetically experiment with creative alternatives, increase learners’ sense of agency, and overturn the notion that we are powerless to change our circumstances. Such pedagogies promote perspective shifts toward solidarity, compassionate accountability, and an increased awareness of the interconnectivity of our collective survival and well-being.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".