Canada's history wars: indigenous genocide and public memory in the United States, Australia and Canada
Bibliographic record
Abstract
In this article, I explore the slow development of a national debate in Canada about genocide in the Indian residential schools, which I compare to earlier ‘history wars’ in Australia and the United States. In the first section I begin with a brief introduction to the history of the IRS system and some of its legacies, as well as attempts at redress. These include financial compensation through the 2006 IRS Settlement Agreement, an official apology and the creation of a Truth and Reconciliation Commission (TRC), which has been a nodal point for articulating claims of genocide. I follow this in the second section with an analysis of the history wars in the United States and Australia over indigenous genocide, before engaging in the third section with debates about genocide in Canada. Overt debates about genocide have been relatively slow in developing, in part because of the creation of a TRC, mandated with collecting the ‘truth’ about the IRS system while similarly engaging in ‘reconciliation’ (a contested term) with settler Canadians. While Canada's history wars may seem slow in getting off the ground, the TRC's more ‘balanced’ approach and wide-ranging engagement with non-Aboriginal societal actors may have a greater effect in stimulating national awareness than in the United States and Australia.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".