Uncovering the Experiences of Engaging Indigenous Knowledges in Colonial Structures of Schooling and Research
Bibliographic record
Abstract
In response to the Truth and Reconciliation Calls to Action (TRC, 2015), a school board teamed with university educators and educational partners to generate a professional learning series to support educators’ engagement with Indigenous knowledges. A research team that assembled two years later interviewed the learning series participants to explore how educators were navigating Indigenous knowledge within a Eurocentric school system. This research acknowledges the challenges of doing this work within shifting institutional policies and initiatives, the wider politics of Indigenous and non-Indigenous relations, building intercultural understandings and community partnerships, and negotiating epistemological difference. The researchers — including Indigenous and non-Indigenous peoples — echoed resonances with the participants that occurred throughout the data collection process and often spoke about the parallel paths of research and schooling — both historically used as tools of colonization and now having a role in decolonization. To disrupt colonial propensities, we share our reflections as researchers, specifically around complexities and tensions of engaging Indigenous knowledges throughout our research processes concerning the participants’ experiences. By sharing the tensions and (un)learning that emerged on these parallel paths, we honour diverse entry-points and experiences to animate how trans-systemic knowledge building might ensue.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.074 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.005 | 0.011 |
| 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".