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Record W4300570849 · doi:10.1093/jahist/jaw360

Colonial Genocide in Indigenous North America

2016· article· en· W4300570849 on OpenAlexaboutno aff
Paige Raibmon

Bibliographic record

VenueJournal of American History · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideIndigenousColonialismFraming (construction)HistoryEconomic JusticeLawCommissionPolitical scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.255
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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