Tracing settler state responsibility for structural harm: Canadian Human Rights Tribunal case on First Nations child welfare
Bibliographic record
Abstract
This article discusses ideas of causation and responsibility for long-term, structural harm perpetuated through settler colonial institutions. Examining the case of a human rights complaint over discrimination of First Nation children in Canada's child welfare system, the article looks at challenges of accommodating such complex harms in the framework of legal liability emphasizing immediate causation and impact. In 2016, the Canadian Human Rights Tribunal ruled that the federal government's funding policy of child welfare services on First Nation reserves leads to disproportionate removals of children from their homes, and in 2019 it ordered the government to financially compensate children and caregivers impacted. According to the government's defense no direct connection could be proved between the funding policy and the removals, and harm resulting from the removals could only be assessed individually. The article traces how the decisions challenge these arguments by extending the scope of the harm at stake from individual removals to historical and ongoing formation of circumstances that make them happen. Setting the case in the historical context of intergenerational harm caused to First Nation kin and community relations by colonial policies, I suggest, the decisions challenge discourses that locate responsibility for such harms in the past.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.066 | 0.031 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".