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Record W2465435214 · doi:10.7202/1036085ar

Being and becoming Inuit in Labrador

2016· article· en· W2465435214 on OpenAlexafffundvenueabout
John C. Kennedy

Bibliographic record

VenueÉtudes/Inuit/Studies · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMetisIdentity (music)PoliticsEthnologyPower (physics)GeographyGenealogyPolitical scienceWork (physics)Gender studiesArchaeologySociologyHistoryLaw

Abstract

fetched live from OpenAlex

Longitudinal research enables discussion of some of the consequences of Aboriginal organizations and identity politics for the Inuit and mixed Inuit-European peoples of Labrador. Newfoundland and Labrador Indians formed the province of Newfoundland and Labrador’s first Aboriginal organization, soon followed by a second, Inuit organization. The mixed Inuit-European “Settlers” (or Kablunângajuit) of northern Labrador initially preferred the Indian organization but were pressured to join and later would dominate the Inuit organization. Moreover, under the 2003 Inuit land claim, Kablunângajuit would legally be considered Inuit. Further south, people of similar mixed Inuit-European ancestry who long denied their Aboriginal roots would organize as Metis. Concurrent with the more than 40 years of identity politics, summarized by this paper, were major international and regional socio-economic changes that saw people move from local to distant work, creating new contexts for identity management. The paper shows how identity politics has changed relations of power and identity, has increased the numbers of people who are legally Inuit or aspire to be so, and, more generally, empowers Aboriginal people to shape their future.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.556
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.081
GPT teacher head0.407
Teacher spread0.326 · 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

Citations8
Published2016
Admission routes4
Has abstractyes

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