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Record W3135860728 · doi:10.3138/chr.2019-0057

The Indigenous Casualties of War: Disability, Death, and the Racialized Politics of Pensions, 1914–39

2021· article· en· W3135860728 on OpenAlexvenueaboutno aff
Eric Story

Bibliographic record

VenueCanadian Historical Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBattlefieldState (computer science)NationalismPatriotismGovernment (linguistics)PoliticsPensionCompensation (psychology)Political scienceSpanish Civil WarCriminologyLawSociologyHistoryPsychologyAncient history

Abstract

fetched live from OpenAlex

The First World War inflicted suffering upon hundreds of thousands of Canadian families between 1914 and 1918. In response, the state modernized its pension system to partially alleviate the postwar suffering of these families, reflecting the changing role of government in the lives of Canadians. To receive a pension after the war, Canadian veterans and dependants had to prove their postwar suffering arose directly from the battlefield, yet not all who qualified were accorded the same treatment. Unlike their non-Indigenous counterparts, external administrators were appointed to oversee the expenditure of pensions given to Indigenous veterans and dependants to ensure they were spent responsibly. Disabled Indigenous veterans and dependants recognized this as a profoundly discriminatory system – reducing them to their “Indian” identity – and drew from the nineteenth-century language of imperial nationalism and patriotism to demand equitable compensation and treatment from the state. Understanding the experiences of death and disability as intimately as the racist discrimination they faced, they envisioned their place as equals within the larger community of Canadian war casualties even though settlers and the state refused to recognize them as such.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2021
Admission routes2
Has abstractyes

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