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Record W4377942449 · doi:10.3389/fmars.2023.1045887

The use of influential power in ocean governance

2023· article· en· W4377942449 on OpenAlexaff
Bianca Haas, Aline Jaeckel, Angelique Pouponneau, Randa Sacedon, Gerald G. Singh, Andrés M. Cisneros‐Montemayor

Bibliographic record

VenueFrontiers in Marine Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
FundersOcean Nexus Center, EarthLab, University of WashingtonAustralian Research CouncilEarthLab, University of Washington
KeywordsNegotiationCorporate governancePower (physics)Perspective (graphical)Inclusion (mineral)Field (mathematics)Political sciencePublic relationsSociologyEngineering ethicsEngineeringManagementEconomicsSocial scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Ensuring inclusivity, especially the meaningful participation of diverse actors, is a key component of good governance. However, existing ocean governance frameworks have not yet achieved an equitable and fair playing field and are indeed often characterized by inequitable practices. In this perspective piece, we argue that one of the reasons for this lack of inclusion are the existing power frameworks and ways in which power is exercised within fora nominally intended to foster inclusion and cooperation. By focusing on four case studies of basic ocean governance processes, we explore how influential and interactive power is exercised in intergovernmental meetings, international conferences, and regional negotiations. These case studies demonstrate how specific exercises of power that undermine procedural inclusivity influence decision-making and the setting of agendas, and exclude important voices from ocean governance fora. This perspective piece contributes to the existing literature on power by highlighting how power is exercised within fundamental aspects of ocean governance. This paper merely scratches the surface, and more actions and research are needed to uncover and, more importantly, reverse deeply-rooted and self-perpetuating power structures in ocean governance.

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 categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.008
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 head0.202
Teacher spread0.194 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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