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Record W4361862852 · doi:10.1093/isagsq/ksad010

“Mother of the Oceans”: Maritime Governance as a Template for a New Global Order in the International Thought of Elisabeth Mann Borgese (1918–2002)

2023· article· en· W4361862852 on OpenAlexaffabout
Lucian M. Ashworth

Bibliographic record

VenueGlobal Studies Quarterly · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHumanityNegotiationCorporate governanceAnthropoceneOrder (exchange)Global governanceEnvironmental ethicsWork (physics)Political scienceSociologyLawPoliticsPhilosophyEconomicsManagementEngineering

Abstract

fetched live from OpenAlex

Abstract This article explores the international thought of Elisabeth Mann Borgese (1918–2002), a major figure in the third United Nations Conference on the Law of the Sea negotiations and (later in her life) a professor at Dalhousie University. Borgese's analysis of the nature of the Ocean led her to see the emerging system of maritime governance as a template for wider global governance. The fluidity of the Ocean, she argued, blurred terrestrial certainties, while the fundamental interdependence of its ecosystems means that its governance offers a new paradigm that can inform terrestrial governance. The Ocean has always been important, she argued, but that importance is now increasing. Thus, in Borgese's work, the Ocean emerges as more than a passive victim of human exploitation, and becomes a positive influence on humanity's future. Taking her work seriously helps international relations (IR) confront its own failure to engage with global physical realities and would be another step toward rewriting an IR for the Anthropocene.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.024
Scholarly communication0.0060.003
Open science0.0000.002
Research integrity0.0020.004
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 head0.016
GPT teacher head0.297
Teacher spread0.282 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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Same venueGlobal Studies QuarterlySame topicInternational Maritime Law IssuesFrench-language works237,207