Reconciliation and Cultural Genocide: A Critique of Liberal Multicultural Strategies of Innocence
Bibliographic record
Abstract
Abstract The aim of this article is to interrogate the concept of cultural genocide. The primary context examined is the Government of Canada's recent attempt at reconciliation through the Truth and Reconciliation Commission. Drawing on the work of Audra Simpson (Mohawk), Glen Sean Coulthard (Yellowknives Dene), Kyle Powys Whyte (Potawatomi), Stephanie Lumsden (Hupa), and Luana Ross (Confederated Salish and Kootenai Tribes, located at Flathead Indian Reservation in Montana), I argue that cultural genocide, like cultural rights, is depoliticized, thus limiting the political impact these concepts can invoke. Following Sylvia Wynter, I also argue that the aims of “truth and reconciliation” can sometimes serve to resituate the power of a liberal multicultural settler state, rather than seek systemic changes that would properly address the present-day implications of the residential school system. Finally, I argue that genocide and culture need to be repoliticized in order to support Indigenous futurity and sovereignty.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.172 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".