University Teaching of Indigenous Genocides in North America: A Pedagogical Perspective on Teaching “Our Own Case”
Bibliographic record
Abstract
I explore the specific challenges of teaching genocides committed in the United States and Canada against Indigenous peoples through various methods and the very logic of settler colonialism. The positionality of settler professors and students means that this subject stands in sharp contrast to other commonly taught cases of genocide which are “far away” physically, psychologically, and often temporally. Teaching Indigenous genocide in North America involves the fraught experience of educating students about the intentional elimination of Indigenous communities by us on a homeland claimed by Indigenous peoples and settler states and societies. These efforts are further challenged by scholars and activists who reject the study of Indigenous genocide in both countries as “woke” campus culture intended to undermine Western values and civilization. Incorporating Indigenous genocides into genocide studies courses can be facilitated by making explicit our own positionality as settler instructors, avoiding cultural appropriation and making Indigenous students feel they must salve our conscience, careful case selection, and respectful collaboration with local Indigenous Elders and Knowledge Keepers built on the sensitive cultivation of community relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".