A Case of Senator Lynn Beyak and Anti-Indigenous Systemic Racism in Canada
Bibliographic record
Abstract
On March 7, 2017, Canadian Senator Lynn Beyak stood up in the Red Chamber and delivered a lengthy speech urging Canadians to recognise the positive aspects of the Indian Residential Schooling system that the Truth and Reconciliation Commission had failed to acknowledge. In their positions as settler teacher educators, the authors examine how Senator Beyak’s statements expose the depth of systemic settler colonialism, anti-Indigenous racisms, and unsettling beneficiary narratives here in Canada. The authors call on teacher educators to examine these systemic anti-Indigenous racisms in relation to how they can confront and disrupt settler Canadian colonialism and historical settler consciousness within teacher education and school curricula. Drawing on recent research done by educational researchers at Faculties of Education across Canada, the authors maintain that settler colonial benevolence and colonial systemic anti-Indigenous racisms can be unlearned and learned through ethical relationality, truth, and a critical praxis of reconciliation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.111 | 0.024 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".