Decolonial and antiracist teacher education practice: Challenges and alternatives
Bibliographic record
Abstract
Abstract Decolonial and antiracist perspectives offer critical and humanizing approaches to supporting justice‐affirming language teacher education. In this commentary, we provide a conceptual grounding for decolonial and antiracist pedagogies as constitutive of justice‐affirming language education. These pedagogical approaches encourage students, teachers, and teacher educators to question normalized assumptions that reinforce inequality among groups of people from diverse backgrounds, perpetuate colonial oppression of Indigenous peoples, and undermine our relationality and respect for land and environment. While decolonial and antiracist approaches envision the construction of more just societies and human relations, some caveats need to be addressed and overcome. These include the tendency to conflate decoloniality with social justice, which leads to neglecting the ongoing colonial oppression experienced by Indigenous people; scholars’ complicity with the neoliberal pressure and competition that exacerbate the theory–practice gap; the misconception that North American justice discourse is universal; and injudicious participation in cancel culture as an exclusive approach to promoting a social justice agenda. We advocate for more open, contextual, and restorative practice by centering the intertwined synergy of teacher identity and critical reflexivity in teacher education and, simultaneously, demanding that our institutions take equal responsibility for transformation.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".