Child-Taking Justice and the Federal Indian Boarding School Initiative
Bibliographic record
Abstract
The focus of this article is the 2022–2024 Federal Indian Boarding School Initiative undertaken the U.S. Executive Branch. The article chronicles this three-year process, which included sessions with survivors and their descendants, and which resulted in a two-volume report, in an apology by President Joe Biden, and in designation of a national memorial at one of the most notorious school sites. This article examines the initiative as an example of “child-taking justice”; that is, as a process of what is called “transitional justice”, done in an effort to redress the takings of children from their community, followed by efforts to alter, erase, or remake the children’s identities. The initiative shed glaring light on the past history and present effects of a centuries-old practice by which the United States took Indigenous children from their families and forced them to attend residential schools where they were compelled to submit to Westernized and Christianized notions of “civilization.” Unfolding within the internal constitutional framework of the United States, the U.S. initiative benefited from meaningful engagement with affected communities. This article nonetheless argues for a framing that also addresses external frameworks; to be specific, one that engages fully with applicable international law and lessons learned elsewhere. The argument runs counter to the United States’ longstanding practice of holding international human rights law at arm’s length, while pressing other countries to conform to that law’s strictures. Efforts of a U.S. human-rights-at-home movement have not reversed that trend. Thus the U.S. initiative made only a hesitant overture to international issues and to three countries, Canada, Australia, and New Zealand, with which it claimed kinship. The 2025 inauguration of a President hostile to rights-based justice pointed to limitations of this approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| 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".