Deepening Knowledge to Inspire Action: Including Aboriginal Perspectives in Teaching Practice
Bibliographic record
Abstract
Deepening Knowledge Project, through Ontario Institute for Studies in Education (OISE), undertook research within the Initial Teacher Education program to explore the relationships between teacher candidates and Aboriginal content. Our research question was, "Which strategies used within OISE’s Central cohort are most powerful in increasing teacher candidates’ willingness and readiness to incorporate Aboriginal knowledges and pedagogies into their classroom practice?" Data consisted of surveys administered to approximately 70 teacher candidates at three key points in their program as well as two rounds of interviews with five purposively selected participants. We found that teacher candidates most appreciated the inclusion of First Voice perspectives, in-depth instruction on current and historical events, and a continuous examination of privilege as means to prepare them for incorporating Aboriginal content into their future practice. While most students reported feeling more confident and willing to include Aboriginal perspectives near the end of their program, there are three commonly stated questions, reported on preprogram surveys that lead to inaction on Aboriginal inclusion. Addressing these questions directly should help encourage more teachers to take up Aboriginal perspectives in their classrooms. Keywords: Indigenous knowledge; teacher education; teacher resistance
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".