Centering Voices: Weaving Indigenous Perspectives in Teacher Education
Bibliographic record
Abstract
The value of weaving Indigenous perspectives into the mainstream curricula of Ontario teacher education programs is gaining prominence (Bell & Brant, 2015; Nardozi, Restoule, Broad, Steele, & James, 2014; Tanaka, 2016). Since the Truth and Reconciliation Commission of Canada’s Calls to Action (2015), efforts are being made across Ontario to “educate teachers on how to integrate Indigenous knowledge and teaching methods into classrooms” (TRC #62, p. 7). Despite growing efforts within teacher preparation programs, many settler teacher candidates are still anxious (Kanu, 2011; Morcom & Freeman, 2019); they fear practicing inadvertent cultural appropriation, and/or offending or misinforming their students and colleagues. To address these concerns, we posed the research question: What impact would Indigenous guest speakers and workshop leaders have on helping Settler teacher candidates navigate Indigenous content in a culturally appropriate and respectful manner? Using an action research framework, we explored how Indigenous ways of knowing impacted the attitudes of teacher candidates in a Bachelor of Education program. The data we collected suggests that by listening to and learning from Indigenous teachings, teacher candidates can attain a deeper understanding of relationality (Wilson, 2008) as it applies to Indigenous ways ofknowing. While certain questions remained, pre-service teachers had an increased knowledge of Indigenous content, and felt more comfortable integrating Indigenous perspectives into their classroom practice.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.046 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".