Responding to survivors: confronting epistemicide within genocide education in Canadian post-secondary institutions
Bibliographic record
Abstract
This thesis promotes Indigenous-led education to address genocides by Canada against Indigenous Peoples of Turtle Island and aims to advance supportive, equitable, and liberatory community relationships. My focus is on how genocide educators, the Canadian post-secondary institutions they work in, and the wider community connected to them are practicing relationality and might build on the practices examined. This desire to learn comes out of the knowledge that better responses to Survivor redress are needed. Genocide education in Canadian post-secondary institutions has a colonial problem that needs further unsettlement. This colonial problem includes an attempt to violently erase Indigenous Peoples both inside and outside of the academy in both material and symbolic ways. A significant part of this colonial problem in the educational context is epistemicide which involves the destruction of Indigenous knowledges and existence, which are inextricably intertwined. The Truth and Reconciliation Commission (TRC) and the National Inquiry into Missing and Murdered Indigenous Women, Girls, and 2SLGBTQQIA+ people (NIMMIWG2S +) have helped bring the discussion of settler colonial genocide in Canada to the forefront. These bodies have called for improved education that centres Survivors on the violence of Canadian settler colonialism, giving impetus to the need for assessment of the progress to date in genocide education in Canadian post-secondary institutions. This thesis explores the question: How are Canadian settler colonial genocides included in or excluded from post-secondary genocide education? It does so with the intent to inform future educational practice. Through my assessment of genocide program and course curricula related to genocides as used in Canadian higher education, I look for patterns and meaningful practices that form relationships with the territories in which it is being taught, as well as the Indigenous Peoples who have lived there since time immemorial. I then draw on theories of settler colonialism and Indigenous methodologies to better understand how these observations might connect to relational accountability. My results show that some practices are connected to ethical relationality, yet overall, much change is needed to confront epistemicide and centre Survivors.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.059 | 0.031 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".