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Record W1974806297 · doi:10.3167/sib.2007.060202

Indigenousness and the Mobility of Knowledge: Promoting Canadian Governance Practices in the Russian North

2007· article· en· W1974806297 on OpenAlexaboutno aff
Elana Wilson

Bibliographic record

VenueSibirica · 2007
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsIndigenousMainstreamSubsistence agriculturePoliticsCorporate governancePolitical scienceSociologyNatural resourceTraditional knowledgeEconomic growthGeographyLawManagementEcologyAgricultureEconomics

Abstract

fetched live from OpenAlex

This article illustrates ways in which a Canadian international development team attempted to legitimate the transfer of natural resource management and economic development models from the Canadian to the Russian North by positing the notion of fundamental similarities between Canadian and Russian northern indigenous peoples. Drawing upon interviews and my participation in the development project, I demonstrate ways in which Russian northern leaders responded to these supposed shared features and describe how the definition of indigenous was debated by Canadian and Russian project participants. Namely, indigenous project participants disagreed over whether indigenousness was rooted in descent or activity and what kind of economic future (mainstream market-oriented or rooted in subsistence practices) could sustain indigenous peoples. I conclude that indigenousness as a unity discourse may facilitate good international politics, but does not serve as an unproblematic mechanism for knowledge transfer and crosscultural communication on a level closer to home.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.018
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.384
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2007
Admission routes1
Has abstractyes

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