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Record W4404115977 · doi:10.1017/s0032247424000172

Six activities of Observers in the Arctic Council

2024· article· en· W4404115977 on OpenAlexaff
Andrew Chater

Bibliographic record

VenuePolar Record · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsOntario Stroke Network
Fundersnot available
KeywordsArcticThe arcticEnvironmental scienceGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract There has been great popular and scholarly interest in the activities of non-Arctic actors in the Arctic region, and in the Arctic Council specifically. We find controversy around the activities of Observers in the Council, with some seeing challenges to Arctic states and others seeing positive co-operation. The Arctic Council is the preeminent governance forum for the Arctic region, consisting of the Arctic states (as of 2023, minus Russia) and six Indigenous peoples’ organisations. Non-Arctic states, intergovernmental organisations and non-governmental organisations can be Observers in the institution. Existing literature has examined the significance, interest and powers of these actors; this paper answers the research question, what do Observers actually do in the Arctic Council? To answer this question, this paper presents the results of content analysis of official Arctic Council Observer reviews and reports, which catalogue their activities. The answer may seem obvious: Observers observe. However, Arctic Council Observers do more than this simple function. This paper proposes that all of the activities of Observers fit into a typology of six types of activity. The ultimate finding is that Observers in the Arctic Council work with Arctic states to enhance institutional work around climate change and sustainable development; we see examples of positive co-operation that enhances regional governance. It is another example of peaceful international relations in the Arctic.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.085
GPT teacher head0.322
Teacher spread0.236 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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