Indigenous Community Members and Settler Teachers: Settler Teacher/Community Member Collaborations to Enhance Teacher Practice
Bibliographic record
Abstract
Settler Teacher (ST) and Indigenous Community Member (CM) relationships are occurring in education settings across the province of Ontario. This project explores the challenges and possibilities of the ST/CM collaboration and how they enhance teacher practice and contribute to decolonizing education, centering Indigenous knowledge, and fostering systemic transformation. To focus on the historical and current effects of colonialism, coupled with settler ignorance in education settings, I have located my positionality as a white woman engaging with Indigenous education. The project is grounded in decolonizing theory and examines relations to knowledge, others, self, and land, offering a framework to appreciate and interrogate ST/CM collaborations. The project situates these collaborations within professional learning theories and Indigenous education research. Critical Indigenous Research Methodology (CIRM) with a narrative inquiry approach was utilized in the project. Five pairs of purposively selected ST/CM pairs who worked, or were working, in Ontario schools participated in the project, using semi structured oral history interviews and conversations. The core insight is that strong, trusting and respectful ST/CM collaborations is a foundational aspect of systemic change in Indigenous education but requires sustained effort and practice. While much research speaks to the importance of relationality, this study gestures to the how of relationships. Participants identified six relational foundations of trusting and respectful collaborations, including listening, awareness of positionality, willingness to un/learn, stepping up/forward/back, bridging gaps, and holding hope. Recommendations within the dissertation invite STs and CMs to continue to share their voices, together or individually, and strongly recommends that researchers, schools and school boards consider how their research and practice can engage with and deepen ST/CM collaborations. Fostering quality ST/CM collaborations to informs teacher practice and supports Indigenous education will require a deep understanding of the ST/CM relationship and a deep understanding of living into trust and respect.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".