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Record W4220996208 · doi:10.3389/fmars.2022.831868

Who Gets the Catch? How Conventional Catch Attribution Frameworks Undermine Equity in Transboundary Fisheries

2022· article· en· W4220996208 on OpenAlexaff
Ruth Davis, Quentin Hanich, Bianca Haas, Andrés M. Cisneros‐Montemayor, Kamal Azmi, Katherine Seto, Wilf Swartz, Pedro C. González‐Espinosa, Mathieu Colléter, Timothy Adams

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaDalhousie UniversitySimon Fraser University
Fundersnot available
KeywordsEquity (law)NegotiationFishingBusinessFisheryFisheries managementSovereigntyAttributionFisheries lawInternational lawEnvironmental resource managementPolitical scienceEconomicsLawPolitics

Abstract

fetched live from OpenAlex

The focus on flag States for the purpose of attributing fisheries catch is inconsistent with the assignment of sovereign rights to coastal States under international law and undermines equity in contemporary quota allocation negotiations. We propose modernizing reporting frameworks to include zone-based reporting of fish catches to more equitably present data, ensure consistency with the Law of the Sea, and better support the realization by developing coastal States of their development aspirations consistent with SDG 14, Life Below Water. States are already required to collect the data necessary to support this change, and many regional fisheries management authorities already do so. Reforms to data collection and reporting mechanisms should support zone-based catch attribution as a central feature of negotiations around access to future fishing opportunities on shared resources. Doing so will ensure that the sovereign rights of developing coastal States are properly accounted for and implemented.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.012
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 designObservational
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

Citations14
Published2022
Admission routes1
Has abstractyes

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