Civil Claims for Uncivilized Acts: Filing Suit Against the Government for American Indian Boarding School Abuses
Bibliographic record
Abstract
This article discusses how, from the late 1800s through the early 1970s, the American government removed American Indian children from their parents and placed them in government-run boarding schools as part of the plan to decimate American Indians as a distinct people. It discusses the schools' inhumane living conditions as well as the abuse suffered by young children as the government implemented its de-culturization plan through these schools. The article then discusses potential civil claims available to boarding school attendees, including claims under the Tucker Act and FTCA, as well as international law claims. It also briefly reviews Canadian governmental attempts to aid boarding school litigants and urges the United States to undertake similar action, or to consider other ways to begin to redress some of the wrongs inflicted upon boarding school survivors. The article concludes by arguing that although centuries of genocidal conduct cannot be redressed by litigation of the boarding school cases, cases brought by boarding school attendees can vindicate individual litigants and pursuit of these claims also can serve to bring the long-hidden horrors of the government's abusive and horrendous treatment of American Indian children into the public consciousness and dialogue.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".