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Record W2163265179 · doi:10.2105/ajph.2011.300584

Global Health—A Circumpolar Perspective

2012· article· en· W2163265179 on OpenAlexaff
Susan Chatwood, Peter Bjerregaard, T. Kue Young

Bibliographic record

VenueAmerican Journal of Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsInstitute for Circumpolar Health Research
Fundersnot available
KeywordsCircumpolar starIndigenousArcticGeographyGlobal healthThe arcticPopulationPopulation healthSouthern HemisphereHealth equityEconomic growthEnvironmental healthEcologyMedicineOceanographyHealth careBiology

Abstract

fetched live from OpenAlex

Global health should encompass circumpolar health if it is to transcend the traditional approach of the "rich North" assisting the "poor South." Although the eight Arctic states are among the world's most highly developed countries, considerable health disparities exist among regions across the Arctic, as well as between northern and southern regions and between indigenous and nonindigenous populations within some of these states. While sharing commonalities such as a sparse population, geographical remoteness, harsh physical environment, and underdeveloped human resources, circumpolar regions in the northern hemisphere have developed different health systems, strategies, and practices, some of which are relevant to middle and lower income countries. As the Arctic gains prominence as a sentinel of global issues such as climate change, the health of circumpolar populations should be part of the global health discourse and policy development.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0040.010
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0070.001

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 head0.038
GPT teacher head0.394
Teacher spread0.355 · 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 designNot applicable
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

Citations35
Published2012
Admission routes1
Has abstractyes

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