INDIGENIZING TEACHER EDUCATION: AN ACTION RESEARCH PROJECT
Bibliographic record
Abstract
This action research report focuses on a new elective course entitled “Indigenizing Education: Education for/about Aboriginal Peoples” that was developed and taught by two teacher educators—one Euro-Canadian and the other Métis. The purpose of the course was to increase understanding of Indigenous peoples and of the impact of colonization on Aboriginal communities. The course had an experiential orientation: participation by an Aboriginal Elder, educators working in Aboriginal settings, and educators who incorporate Indigenous knowledge and pedagogy into mainstream classrooms. Action research was conducted to determine the degree to which the course achieved its purpose with a view to enhancing future iterations of this course and contributing to teacher educator knowledge about effective teacher education approaches to including Aboriginal content and ways of knowing in teacher education. In particular, the authors were interested in the degree to which teacher candidates were responsive to Indigenous elements in the curriculum and teaching. Given the Aboriginal focus of this research, the Medicine Wheel is employed as an Indigenous framework for presenting and analyzing the findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".