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Record W4412708611 · doi:10.1177/23996544251360540

Arctic circles: Transprofessional networking and international governance

2025· article· en· W4412708611 on OpenAlexafffund
Merje Kuus

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArcticCorporate governanceEthnographyThe arcticSociologySocializationPoliticsWork (physics)Political sciencePublic relationsSocial scienceOceanographyManagementEngineeringAnthropologyLaw

Abstract

fetched live from OpenAlex

Arctic-focused networking events are gaining prominence as sites of transnational and transprofessional governance. Central among such events is the Arctic Circle Assembly, an annual conference that takes place in Reykjavik in October. This paper uses Arctic conferences in general and the Arctic Circle Assembly in particular to examine transnational and transprofessional knowledge-creation, a process that requires regular interaction across national and professional boundaries. Conceptually, the paper draws on political geography, international relations, and anthropology to investigate interaction, socialization, and sociability in international governance. Empirically and methodologically, it combines participant observation at eight major Arctic meetings in 2022-25 with over thirty in-person interviews with those who organize and attend such events. My principal focus is not on the big picture of the meetings' goals but on the small details of their social milieu. I seek to give a 'peopled' or ethnographic account of the events to foreground the social workings of transprofessional interaction. The question to ask is not only how Arctic networks work and how they matter but also how the Arctic networking events work and how they matter. Beyond the Arctic, the paper advances our understanding of the spaces and practices of international governance.

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 score0.418
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.297
Teacher spread0.277 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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