Transforming the (Teacher) Educator Through Ecojustice and Decolonization
Bibliographic record
Abstract
Transformative Inquiry (Tanaka, 2015) can be a methodological ally in critical education that addresses systems of oppression. Transformative Inquiry (TI) actively decenters institutional knowledge by placing one’s intuitions and embodied experiences and classroom observations on an equal footing with the academic literature. The process draws heavily upon Indigenous methodologies and pedagogies, specifically, relational accountability. This paper reports how using TI challenged the author’s previously held Western cultural beliefs around scientism and individualism. It also reports on selected experiences from the author’s teaching career, salient public pedagogy moments and conversations with fellow critical ecojustice educators that helped decolonize her thinking and made her more aware of eco-cidal neoliberal structures often overlooked, like the Janus-face of science. Data are still being collected as the research process is (and always will be) ongoing. However, initial results point to an increased understanding of unjust local power structures and habits of mind and the need for courage in pointing them out. These results also suggest the necessity of speaking out in allyship with oppressed groups who may not have a voice and using the author’s white privilege to make and hold space for everyone to be heard. This study is about how one scientist-turned-educator used TI to learn from and with others about how to decolonize her mind and unlearn Western, eco-cidal, neoliberal norms that have created the conditions for current socio-ecological crises.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".