Action Research: Differentiating Indigenizing and Decolonizing in Teacher Education
Bibliographic record
Abstract
The purpose of this paper is to discuss how action research provides a means to differentiate indigenizing and decolonizing in teacher education. This study follows the traditional AR cycle of planning, acting, observing and reflecting and is also informed by transformational grounded theory which combines particular elements of action research, constructivist grounded theory, and decolonizing research methodologies. As the teacher-researcher, I kept a reflective journal, wrote memos and used my learning plans and activities as a source of data. Three critical friends, one Indigenous scholar and two decolonizing scholars supported my reflective processes as well. After the course was over, seven students participated in individual, semi-structured audio-taped interviews. Indigenization and decolonization are not the same. Indigenizing is about changing what know. Decolonizing is changing how we know. Decolonizing in our classroom then requires inviting PSTs to (re)consider their relationship with land, cultures, languages and traditions. How we know, our relationship to what we know, is the barrier; these are questions that emerged as we explored elementary curriculum. Action research enabled me to change my practice in full view, and alongside, my students as we reconceptualised curricula, content, evaluation and relationships.
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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 | 0.105 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 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".