Navigating the paradox of repair: Indigenous genocide and public education in Minnesota and Manitoba
Bibliographic record
Abstract
This paper examines how educational institutions and individual educators in Manitoba, Canada, and Minnesota, United States (US), understand and enact reparation in their respective fields of practice. The basic principle of restorative or reparative justice is that wrongs are set right by giving back to the injured party that which restores equality, dignity, and rights. However, educational reforms reveal a structural challenge in societies reckoning with the legacies of foundational violence perpetrated against Indigenous populations. Coupling an analysis of state / provincial social studies standards with key informant interviews, we try to untangle the paradox that the public education system, including non-formal education offered in state museums or historical societies are now tasked with addressing and repairing an injustice they were- and, to a large degree, continue to be - a part of. Findings show that sometimes dissonant visions of a reparative future and conflicting attempts to seek and promote justice in public history, the curriculum, and pedagogy are negotiated in the educational arena, resulting in complementary forms of specific practices. These constitute a growing stock of knowledge and models of engagement that shed light on how institutions and individual actors navigate the project and paradox of repair.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".