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Record W4238257114 · doi:10.31230/osf.io/hrb8g

Utilizing existing legal frameworks to implement fisheries management in the arctic

2018· preprint· en· W4238257114 on OpenAlexaboutno aff
OCTO Open Communications for The Ocean

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFishingArcticFisheryUnited Nations Convention on the Law of the SeaDeclarationInternational watersFisheries managementInternational lawCommercial fishingNegotiationPolitical scienceMarine protected areaTreatyGeographyBusinessOceanographyLawEcology

Abstract

fetched live from OpenAlex

As climate change contributes to accelerated melting of sea ice in the Arctic Ocean, areas that were previously off-limits to fishing will become accessible. While there are currently no internationally-agreed fishing regulations in the high seas of the Arctic, there is an effective moratorium on commercial fishing thanks to the non-binding “Oslo Declaration” (Declaration concerning the Prevention of Unregulated High Seas Fishing in the Central Arctic Ocean, from July 2015). The author investigates existing legal mechanisms which could be used to regulate an international Arctic fishery should commercial fishing begin.Currently, the Arctic Five (Canada, Denmark/Greenland, Norway, the Russian Federation, and the United States) plus Five (Iceland, the EU, China, South Korea, and Japan) are negotiating an agreement for fisheries management in the Central Arctic Ocean. The draft text, which has not reached consensus yet*, surprisingly does not seek to establish a regional fisheries management organization for this area. But, the Arctic 5 + 5 have agreed “to continue the moratorium on fishing on the high seas of Central Arctic until there is scientific evidence concerning sustainable fishing in the area.”The draft agreement further states, “the parties shall take measures consistent with international law to deter the activities of vessels entitled to fly the flags of non-parties that undermine the effective implementation of this Agreement.” But what measures ‘consistent with international law’ could member states adopt?

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)0.043
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0100.014
Scholarly communication0.0150.007
Open science0.0040.008
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.098
GPT teacher head0.400
Teacher spread0.302 · 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 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
Published2018
Admission routes1
Has abstractyes

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