Retracing our roots through story medicine: Re-storying Kahnawà:ke Indian day schools
Bibliographic record
Abstract
This research investigates the history of federal Indian Day Schools from 1868 to 1988 in the Kanien’kehá:ka [Mohawk] community of Kahnawà:ke. It explores the impacts of Indian Day Schools on language and cultural identity transmission through collective oral histories. This work builds on our understandings of the genocidal ‘education’ of Indigenous peoples in Canada, challenging the dominant narrative about Indian Residential Schools. Drawing on Indigenist felt theory by Dian Million, I name the structure of child-focused colonization Child-Targeted Assimilation. I employ a wholistic Indigenous research paradigm, introducing Story Medicine as the foundation and lens of thematic analysis. In addition to numerous community interviews, I integrate my personal experience as a community member, claimant in the Federal Indian Day School Class Action, and descendant of Indian Day School and Indian Residential School survivors. To add further depth and rigor, the study incorporates extensive archival research, situating these narratives within multiple historical contexts. This combination enriches the study and provides outcomes that are both academically rigorous and profoundly relevant to Indigenous history, education, and lived experience. As people of Kahnawà:ke, we re-story our past and confront hard truths to generate pathways through colonial trauma for current and future generations, grounded in our worldview.Keywords: Indian Day School, Indigenous education, Indigenous history, oral history, Kahnawà:ke, Federal Indian Day School Class Action
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.030 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".