Silencing Indigenous Knowledge Systems: Analysis of Canadian Educational, Legal and Administrative Practice
Bibliographic record
Abstract
As a result of the Truth and Reconciliation Commission of Canada (or TRCC, 2015a, 2015b), calls to action concerning education and law reform have been made. Currently, there is an increase in reconciliation discourse in law, healthcare and education policy, curricula and pedagogy. In Canada, efforts to decolonize institutional structures compel scholars and activists to highlight the imperative of critical analysis of identity and place in answering the calls to action. Although it was developed by the Ministry of Education for the province of Ontario, more than a decade ago, prior to the TRCC, the First Nations, Métis, and Inuit Education Policy Framework continues to inform policy and administrative procedures. Informed by Indigenous knowledge systems embedded in restorative justice and peace-building practices, this paper presents a critical analysis of the First Nations, Métis, and Inuit Education Policy Framework (2007) and finds evidence resembling discursive settler-colonial patterns of Indigenous erasure through the practice of silencing Indigenous participation and voice. Through this critical analysis, several themes emerged including colonialism, survivance, patriarchy, self-identification, notions of education, assessment, and “us versus them” binary narratives. In response, this paper argues for a trans-systemic and transdisciplinary approach to the critical analysis of discursive patterns of silencing and erasure in policy, law reform, and administrative processes. Further, through deepening interpretations and understandings of Indigenous theory and knowledge systems, it may be possible for settler-colonial stakeholders to more acutely discern the impact of settler-colonialism embedded in education, policy, administration, and legal discourses. These findings have implications for educators and administrators as well as administrative, law and policy reform.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.043 | 0.024 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".