This Benevolent Experiment: Indigenous Boarding Schools, Genocide, and Redress in Canada and the United States
Bibliographic record
Abstract
At the end of the nineteenth century, Indigenous boarding schools were touted as the means for solving the “Indian problem” in both the United States and Canada. With the goal of permanently transforming Indigenous young people into Europeanized colonial subjects, the schools were ultimately a means for eliminating Indigenous communities as obstacles to land acquisition, resource extraction, and nation-building. Andrew Woolford analyzes the formulation of the “Indian problem” as a policy concern in the United States and Canada and examines how the “solution” of Indigenous boarding schools was implemented in Manitoba and New Mexico through complex chains that included multiple government offices with a variety of staffs, Indigenous peoples, and even nonhuman actors such as poverty, disease, and space. The genocidal project inherent in these boarding schools, however, did not unfold in either nation without diversion, resistance, and unintended consequences. Inspired by the signing of the 2007 Indian Residential School Settlement Agreement in Canada, which provided a truth and reconciliation commission and compensation for survivors of residential schools, This Benevolent Experiment offers a multilayered, comparative analysis of Indigenous boarding schools in the United States and Canada. Because of differing historical, political, and structural influences, the two countries have arrived at two very different responses to the harm caused by assimilative education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".