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Record W277811461

Canada, the EU and Arctic Ocean Governance: A Tangled and Shifting Seascape and Future Directions

2009· article· en· W277811461 on OpenAlexaffabout
David VanderZwaag, Timo Koivurova, Erik Molenaar

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSeascapeCorporate governanceArcticPolitical scienceConventionUnited Nations Convention on the Law of the SeaCommissionGeographyEuropean unionLaw of the seaInternational lawFisheryOceanographyEnvironmental planningBusinessLawPublic international lawInternational tradeEcology
DOInot available

Abstract

fetched live from OpenAlex

The objective of this paper is to examine (in a historical perspective) the roles of the European Union (EU) and Canada in governance and regulation of human activities in the Arctic Ocean. Section two describes the existing “tangled” nature of governance in the Arctic with a focus on law of the sea, approaches and challenges in the region, as well as on EU and Canadian participation in the activities of the Arctic Council. The “shifting seascape” in governance is next highlighted in section three with a review of increasing calls for change from scholars and other groups, recent governance initiatives from the United States and Arctic Ocean coastal states, and evolving EU and Canadian perspectives towards ocean governance. The paper concludes with section four, which surveys possible future directions for strengthening ocean governance in the Arctic, with the spectrum of options including, among others, expanding the spatial scopes of the North-East Atlantic Fisheries Commission (NEAFC), established by the NEAFC Convention, and the OSPAR Commission, established by the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) Convention, and reform by means of an Implementing Agreement under the United Nations Convention on the Law of the Sea (LOS).

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.999

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 studies0.0020.000
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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.

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

Citations10
Published2009
Admission routes2
Has abstractyes

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