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Record W2792975840 · doi:10.36368/jns.v11i1.880

Nuna : Naming the Inuit land, imagining indigenous community

2017· article· en· W2792975840 on OpenAlexaboutno aff
Julien Pongérard

Bibliographic record

VenueDiVA at Umeå University (Umeå University) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFocus (optics)ArcticThe arcticGeographyHistoryEthnologyAnthropologySociologyEcologyOceanographyGeology

Abstract

fetched live from OpenAlex

The Inuit land is often known through Western phrasings such as the “Arctic” or the “Great North.” In this article, based on an extensive review of literature, I focus on the name the Inuit give to their own land, which is one of the only words common to all Inuit dialects: nuna. Studying the word’s meaning casts light on a peculiar indigenous territoriality, and on the centrality of environment in Inuit ways of life and holism thinking. The Inuit conceptualize their inhabiting of the circumpolar region in a way radically opposed to Western narratives of wilderness or wasteland. In the late twentieth century, nuna was turned into a key component of identity politics. Inuit peoples linguistically reappropriated their lands, in parallel with formal land claims and the recognition of Inuit self-governed territories. Nuna is at the core of these processes, as the concept justifies the claims for recognition of vernacular toponyms, and the vocable itself was included in the names of Inuit regions. Nuna as an indigenous political banner helps understanding the imagination of Inuit political communities, emerging from a dialectical co-construction of identities and territories mediated through the linguistics of place.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0750.001
Scholarly communication0.0000.001
Open science0.0020.005
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.306
Teacher spread0.260 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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