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Record W4395019327 · doi:10.31857/s020170832302002x

Arctic science diplomacy of the Nordic Countries

2023· article· en· W4395019327 on OpenAlexaboutno aff
Maxim Gutenev, Alexander Anatol'evich Sergunin, Olga N. Shadrina

Bibliographic record

VenueContemporary Europe · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Science and Diplomacy
Canadian institutionsnot available
Fundersnot available
KeywordsDiplomacyArcticThe arcticPolitical scienceInternational tradeGeographyBusinessOceanographyGeologyPoliticsLaw

Abstract

fetched live from OpenAlex

Arctic science diplomacy (ASD) is one of the innovative tools for promoting a positive image of the state and building strong international partnerships in the region. This study aims to identify common and distinctive features of Swedish, Finnish, Norwegian, Icelandic and Danish ASDs. The article analyses the motivation and main priorities of the Nordic ASD both at the national and regional levels. The state of the ASD infrastructure in each of the Nordic countries is also described. The Nordic states seek to coordinate their Arctic research and create joint structures for these purposes. Since Denmark, Finland and Sweden are the EU members, The Nordic countries are actively using the financial, organizational and intellectual resources of the EU to increase the effectiveness of their ASD. A common feature for all five Nordic countries is also the close attention they pay to ASD as part of their polar strategies. The Nordic countries focus on the ASD since they have much less economic, geopolitical and military resources than such "Arctic giants" than the United States, Canada and Russia. The article also indicates that with the start of the Russian special military operation in Ukraine, scientific cooperation between the Nordic countries and Russia has been significantly reduced on the initiative of these states. This has further complicated the situation in the Arctic region.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.047
GPT teacher head0.352
Teacher spread0.305 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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