Preparing Anti-Racist Educators through Critical Transformative Emotional Praxis
Bibliographic record
Abstract
In settler-colonial countries like Canada, Whiteness—the customs, beliefs, values, and so on that comprise White culture—is the standard to which all others are compared. Whiteness is woven into the very fabric of our society, working to uphold White supremacy and systemic racism. Education, as part of this system, is also fraught with Whiteness, and its deleterious effects are evident in the persistent inequities experienced by students of Colour. Dismantling Whiteness in education is a daunting task, but one promising solution is to develop anti-racist educators capable of embodying and enacting culturally responsive and sustaining pedagogies. However, this requires directly addressing Whiteness in teacher education programs, an endeavour that has proven challenging. As an entry into this topic, this paper explores Whiteness writ large including how it is studied both broadly and within the field of education. Then, approaches to addressing Whiteness in teacher education are reviewed, including what is and is not working. Next, other approaches to teacher education that could ameliorate current efforts to develop anti-racist educators are introduced: transformative learning and critical emotional praxis. Finally, these are woven together in a theory of change to address Whiteness in teacher education and support preservice teachers’ anti-racist development.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".