Bibliographic record
Abstract
That Indigenous people in Canada were victimized for well over a century by the residential schools system for Aboriginal children is not in question. The system, which amounted to an “assault on child and culture,” was designed to “kill the Indian in the child.” Whether the legal system – purporting to provide some form of compensation in the context of claims by survivors and their families – has provided justice is a much more open question. The costs – financial, social, health, time, and so on – associated with pursuing the resolution of residential schools claims through the justice system have been enormous. These costs feed skepticism about the commitment of the justice system to the process of truth and create immense barriers to progress towards reconciliation.\nThe point of this chapter, part of the body of Costs of Justice research, is primarily to call out some of the problematic steps in the various residential schools claims processes that have resulted in and allowed for those costs, and to situate those processes and costs in current ongoing truth and reconciliation efforts in Canada. In addition to the residential schools litigation, I will briefly mention several other problematic cases and contexts to make the point that, when thinking about access to justice, the residential schools litigation is not an isolated incident but rather part of a continuum that many see as costly, unequal, and alienating justice in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.049 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".