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Record W4387743040 · doi:10.1002/sea2.12294

Military wealth: How money shapes <scp>Indigenous‐state</scp> relations among Canadian rangers

2023· article· en· W4387743040 on OpenAlexaboutno aff
Bianca Romagnoli

Bibliographic record

VenueEconomic Anthropology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousArcticColonialismState (computer science)SovereigntyPolitical scienceIdeologyGeographyLawPoliticsOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract Presented as the eyes, ears, and voice for the Canadian Armed Forces in the Canadian Arctic, Canadian Rangers within the first Canadian Ranger Patrol Group (1CRPG) are applauded as being positive and progressive examples of state‐Indigenous relations. Located in almost 70 communities across the Northwest Territories, Nunavut, the Yukon and Atlin, British Columbia (BC), Canadian Rangers in 1CRPG are viewed as a critical part of the arctic defense strategy and a cheap and easy way to maintain arctic sovereignty, especially in predominately Indigenous communities in the high arctic. Focusing on how Rangers and Ranger Instructors talk and think about the pay system, this article examines how value is ascribed to Rangers depending on their ability and desire to financially invest in the organization. Studying the polarity, this article analyzes how the military—which prides itself on employing Indigenous people as part of arctic defense—reinforces colonial ideologies and relational structures of Indigenous communities' dependence on state aid. However, this, I argue, further entrenches dangerous colonial stereotypes that (Indigenous) members make poor economic choices and are thus responsible for continuing their ongoing poverty and inferiority.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.014

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 head0.027
GPT teacher head0.326
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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