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Record W2940094872 · doi:10.1163/22116427_009010002

Speaking of Rights: Indigenous Linguistic Rights in the Arctic

2018· article· en· W2940094872 on OpenAlexaboutno aff
Romain Chuffart

Bibliographic record

VenueThe Yearbook of Polar Law Online · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousDevolution (biology)ArcticIndigenous rightsGovernment (linguistics)Political scienceLanguage planningState (computer science)The arcticSociologyHuman rightsGeographyLawLinguisticsAnthropologyEcology

Abstract

fetched live from OpenAlex

This paper discusses and compares the evolution of language policies, laws and rights for indigenous peoples and minorities living in six of the eight Arctic states. It focuses on language rights of indigenous peoples living in the Fennoscandian Arctic (Sami people of Norway, Sweden, and Finland), in the American Arctic (Alaska) and in the Canadian Arctic (Nunavut, Northwest Territories, and Yukon). This paper also focuses on linguistic rights in Greenland. The aim of this study is to add to the discussion about how the use of indigenous languages in the public sphere (education, the judicial system, and interactions with the government) helps indigenous-language speakers who live in the Arctic to preserve their ways of life and their cultural identities. This paper posits that asymmetrical management is key to fulfilling indigenous linguistic rights. Devolution of language planning and policy implementation to the relevant local authorities often makes sense from a state viewpoint and, although it is not enough, it can be beneficial to indigenous speakers.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0050.004
Open science0.0010.004
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 head0.045
GPT teacher head0.417
Teacher spread0.372 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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