Witnessing the Reversal of Indigenous Erasure: My Undergraduate Experience
Bibliographic record
Abstract
This essay addresses how eighteenth-century studies can be more inclusive of Indigenous scholarship and knowledge, and the possibility of decolonizing the field. It follows an autobiographical narrative of undergraduate courses and a research project concerning the oral traditions and history of the Osage Nation. My experience shows that individual professors’ mentoring and their inclusion of Native American literature, authors, and topics in course offerings are crucial to advancing the decolonization and reversal of Indigenous erasure in university settings. In the field of Indigenous studies and literature, the previous work of ethnologist Francis La Flesche, anthropologist Garrick Bailey, and historian Louis F. Burns on the Osages provides a foundation for future scholarship on this tribe. My conclusion is that the willingness of professors to offer courses on Indigenous literature, or considerations of how Native American issues relate to and influence their field of study, are key to developing BIPOC inclusion in literary academia.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| 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".