Developing Equitable Pedagogical Practices in Teacher Education: Considerations for Critical Transformative Perspectives in a North American Context
Bibliographic record
Abstract
Teacher education programs may encourage their students to reflect upon their own school experiences through critical perspectives to develop equitable pedagogical practices for a better society. However, what are the implications of using critical perspectives? The purpose of this theoretical paper is to examine assumptions of using critical transformative approaches in teacher education for equity by addressing the following question: What issues between teacher educators and their students need to be considered when using a critical transformative learning approach to develop equitable pedagogical practices in a North American context? By framing critical and transformative learning as working with difficult knowledge and cognitive dissonance, I argue that teacher education courses need to create spaces that foster authentic dialogues to move beyond psychologizing to critical awareness for equity in 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 | 0.039 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| 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".