Teaching Legacies of the Carlisle Indian School
Bibliographic record
Abstract
The horrifying news of the discovery of hundreds of graves of children at Native American boarding schools in Canada has a contemporary companion: the tears of Latinx kids on the border in the summer of 2018 (Kelly 2018). You may recognize these voices as those of the immigrant children who were separated from their parents upon crossing the US/Mexico border in the summer of 2018. I’d like you to juxtapose them with any of the thousands of Native American children separated from their parents and forced to attend US-run boarding schools in the nineteenth and twentieth centuries. A different time and different languages, indeed. But the emotion is likely the same: the fear and desperation of dark-skinned children forced to live in the crossroads of US colonization. To highlight this connection I share my experiences teaching a class on boarding schools, which I believe is one effective response to today’s encounters with colonialism. Although I have always included some boarding school material in my Native American Literature survey class at West Virginia University, in the fall of 2019 I had the opportunity to design a one-credit Native American class entitled “Carlisle Indian School Legacies.” As a one-credit course focused on the experience of visiting Carlisle, it was less involved than a normal three-credit class. Therefore I share here some of the literature and assignments I plan to use when teaching a larger version of this course.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".