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Record W2969799656 · doi:10.1080/22423982.2019.1653749

Towards estimating the indigenous population in circumpolar regions

2019· article· en· W2969799656 on OpenAlexafffundabout
T. Kue Young, Peter Bjerregaard

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsCircumpolar starIndigenousArcticGeographyCensusProxy (statistics)PopulationEthnologySocioeconomicsDemographyEnvironmental protectionHistorySociologyEcologyOceanography

Abstract

fetched live from OpenAlex

Despite the importance of indigenous people in the Arctic, there is no accurate estimate of their size and distribution. We defined indigenous people as those groups represented by the "permanent participants" of the Arctic Council. The census in Canada, Russia and the United States records status as an indigenous person. In Greenland, a proxy measure is place of birth supplemented by other information. For the Nordic countries we utilized a variety of sources including registered voters' lists of the various Sami parliaments and research studies that established Sami cohorts. Overall, we estimated that there were about 1.13 million indigenous people in the northern regions of the 8 Member States of the Arctic Council. There were 8,100 Aleuts in Alaska and the Russian North; 32,400 Athabaskans in Alaska and northern Canada; 145,900 Inuit in Alaska, northern Canada and Greenland; 76,300 Sami in northern Norway, Sweden, Finland and Russia; and 866,400 people in northern Russia belonging to other indigenous groups. Different degrees and types of methodological problems are associated with estimates from different regions. Our study highlights the complexity and difficulty of the task and the considerable gaps in knowledge. We hope to spur discussion of this important issue which could ultimately affect strategies to improve the health of circumpolar peoples.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.097
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.407
Teacher spread0.371 · 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

Citations50
Published2019
Admission routes3
Has abstractyes

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