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Record W3195895812

Survey of Living Conditions In The Arctic: What Did We Learn?

2007· article· en· W3195895812 on OpenAlex
Gérard Duhaime, Jack Kruse, Birger Poppel, Larissa Abryutina, Virgene Hanna, Stephanie Martin, Marie Katherine Poppel, Ed Ward, Marg Kruse, Patricia Cochran, Carl Erik Olsen, Heather Meyers

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarWorks - UA (University of Alaska System) · 2007
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsResearch councilDanishPolitical scienceArcticGovernment (linguistics)CommissionCouncil of MinistersPublic administrationSocial researchFoundation (evidence)GeographyLibrary scienceSociologySocial scienceLawEuropean unionOceanography
DOInot available

Abstract

fetched live from OpenAlex

In countries around the Arctic, tens of thousands of Iñupiat, Inuit, and other indigenous peoples live in small, isolated communities where jobs are scarce, incomes are low, and life is not easy. Yet many—including large majorities in Canada, Northern Alaska, and Greenland—are satisfied with life in their communities. That was the puzzle researchers from Statistics Greenland faced in 1994, when they studied living conditions and found that common measures of well-being—like levels of employment—didn’t explain why so many of Greenland’s Inuit chose to stay in their communities. About 7,250 Inuit, Iñupiat, and other indigenous peoples were interviewed in Greenland, Northern Alaska, the Chukotka region of Russia, and the Inuit settlement areas of Canada. The Institute of Social and Economic Research (ISER) conducted the survey in Alaska. This publication describes the survey and introduces the wealth of new information now available on the lives of the Arctic’s first people, measured in ways they themselves chose. Also printed in Valerie Moller, Denis Huschka and Alex Michalos (eds). Barometers of Quality of Life Around the Globe: How Are We Doing? New York: Springer Verlag, 107-134.

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.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.048
GPT teacher head0.324
Teacher spread0.275 · 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