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Record W4285213506 · doi:10.23865/arctic.v13.3233

Canada and the Russian Federation: Maritime Boundaries and Jurisdiction in the Arctic Ocean

2022· article· en· W4285213506 on OpenAlexafffundabout
Viatcheslav Gavrilov, Ted L. McDorman, Clive Schofield

Bibliographic record

VenueArctic review on law and politics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of Victoria
FundersFar Eastern Federal UniversityDalhousie UniversityDonner Canadian Foundation
KeywordsMaritime boundaryJurisdictionArcticThe arcticUnited Nations Convention on the Law of the SeaPolitical scienceInternational lawRussian federationGeographyLaw of the seaOceanographyLawRegional sciencePublic international lawGeology

Abstract

fetched live from OpenAlex

The Arctic region has been the focus of considerable attention in recent years, often concerned with maritime claims and an alleged race for the region’s resources. Against this narrative, the article focuses on the practices of Canada and the Russian Federation with respect to their maritime jurisdictional claims and the delimitation of maritime boundaries with their Arctic neighbours. The article provides an overview of the Arctic region and the international law of the sea with an emphasis on the baselines and maritime claims of the Arctic coastal states. Discussion then turns to the maritime boundary agreements that have been concluded in the Arctic region before overlapping claims to areas of continental shelf underlying the central part of the Arctic Ocean are appraised. The article concludes that Canada and the Russian Federation have enjoyed considerable success in resolving overlapping maritime claims and their pragmatic and innovative approaches coupled with existing regional cooperation bode well for finding peaceful solutions to Arctic Ocean governance challenges in the future.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.052
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0100.015
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.276
Teacher spread0.263 · 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
GenreOther

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
Published2022
Admission routes3
Has abstractyes

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