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Record W2981548241 · doi:10.3167/sib.2019.180301

Indigenous Peoples, Urbanization Processes, and Interactions with Extraction Firms in Russia's Arctic

2019· article· en· W2981548241 on OpenAlexaboutno aff
Marlène Laruelle

Bibliographic record

VenueSibirica · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousArcticCircumpolar starPopulationGeographyUrbanizationThe arcticEthnologyDemographyEcologyHistorySociologyOceanography

Abstract

fetched live from OpenAlex

Russia is unique on the circumpolar landscape in that indigenous communities constitute only a small percentage of its Arctic population. Whereas they represent 80 percent of Greenland’s population, 50 percent of Canada’s, 20 percent of Alaska’s, and 15 percent of Norway’s Arctic regions, they make up less than 5 percent of the population of Arctic Russia. Although indigenous peoples have a more solid demography than Russians and have therefore seen their share of the Arctic population slowly increase over the past two decades, their rights remain fragile. Moscow does not consider the Arctic to have a specific status due to the presence of indigenous peoples, and its reading of the region is still very much shaped by the imperial past, the memory of an easy conquest (osvoenie) of territories deemed “unpopulated,” and the exploitation of the region’s subsoil resources.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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