Bibliographic record
Abstract
How might genocide serve as an organizing principle for the history of indigenous and nonindigenous relations in North America? I asked this question as I read this important and timely collection. The essays span a broad chronological and geographical range, enough to recommend the book's possible use in undergraduate courses. What would be gained and what might be obscured by such a framing? These authors are concerned with this very question. They take varied, sometimes-opposing positions on requirements for the term's applicability, its interpretive value, and its practical utility. All agree that genocide happened during European colonization. The “colonial” in the book's title does not reference the prenational period but more broadly the centuries following European arrival in North America. And the title's “North America” refers to the United States and Canada. These chapters originated at a 2012 conference, “Colonial Genocide and Indigenous North America,” held at the University of Manitoba. The Truth and Reconciliation Commission (trc) enquiry into Canada's residential schools was underway, and the commission chair Justice Murray Sinclair gave the keynote address in which he applied the label “genocide” to residential schools. The trc commissioners offered a somewhat-qualified position in their 2015 final report where they referenced “cultural genocide,” a terminological shift relevant to the questions engaged by these essays.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".