ReBraiding Frayed Sweetgrass for Niijaansinaanik: Understanding Canadian Indigenous Child Welfare Issues as International Atrocity Crimes
Bibliographic record
Abstract
The unearthing of the remains of Indigenous children on the sites of former Indian Residential Schools (“IRS”) in Canada has focused greater attention on anti-Indigenous atrocity violence in the country. While such increased attention, combined with recent efforts at redressing associated harms, represents a step forward in terms of recognizing and addressing the harms caused to Indigenous peoples through the settler-colonial process in Canada, this note expresses concern that the dominant framings of anti-Indigenous atrocity violence remain myopically focused on an overly narrow subset of harms and forms of violence, especially those committed at IRSs. It does so by utilizing a process-based understanding of atrocity and genocide that helps draw connections between familiar, highly visible, and less recognized forms of atrocity violence, which tend to be overlapping and mutually reinforcing in terms of their destructive effects. This process-based understanding challenges the neocolonial, racist, and discriminatory attitudes reflected in the drafting and interpretation of the Genocide Convention and other atrocity laws that ignore the lived experiences of subjugated groups. Utilizing this approach, this note argues that, as applied to Indigenous populations, Canada’s longstanding discriminatory child welfare practices and policies represent an overlooked process of anti-Indigenous atrocity violence. Only by understanding current child welfare challenges facing Indigenous communities as interwoven with longstanding anti-Indigenous atrocity processes, such as the IRS system, can we understand what is at stake for affected communities and fashion appropriate remedies in international and domestic law.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.028 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".