Bibliographic record
Abstract
As political divides escalate across Canada between so-called lefts and rights, we reflect on the “us versus them” mentality perpetuated through the media and what we can do as educators to identify inequities, call out harm, close the gap, and combat colonial logic and white supremacy within teaching and learning. The main research question explores white supremacy and how it is perpetuated in teacher education. We center our counter-stories through reflection and dialogue between a racialized, immigrant, Muslim early career scholar and a racialized, disabled, queer, and non-binary high school teacher. Through duoethnography, we share our identities, vulnerabilities, and lived experiences to discuss our growth over time as educators dedicated to anti-racist and decolonial praxis. This includes how we have perpetuated white supremacy and how we have learned to differentiate our strategies to resist, subvert, and challenge colonial logic within teacher education. We discuss what has helped us cope, unlearn, and grow to call out and undo harm working within hierarchical educational spaces. We outline examples of how current educational systems and their inequitable policies and practices enact harm on learners from queer, trans, Black, Indigenous, and people of color (QTBIPOC) communities. The chapter concludes with recommendations for how educators can question inequitable policies and practices in allyship and solidarity with students to advance reconciliation in settler education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".