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Record W4306668004 · doi:10.1371/journal.pbio.3001828

Living in relationship with the Ocean to transform governance in the UN Ocean Decade

2022· article· en· W4306668004 on OpenAlexaff
Michelle Bender, Rachel Bustamante, Kelsey Leonard

Bibliographic record

VenuePLoS Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHumanityOverfishingEnvironmental ethicsCorporate governanceSustainabilityMultitudeSustainable developmentBiologyEcologyEnvironmental resource managementPolitical scienceLawEconomicsFishing

Abstract

fetched live from OpenAlex

Humanity's relationship with the Ocean needs to be transformed to effectively address the multitude of governance crises facing the Ocean, including overfishing, climate change, pollution, and habitat destruction. Earth law, including Rights of Nature, provides a pathway to center humanity as a part of Nature and transform our relationship from one of dominion and separateness towards holism and mutual enhancement. Within the Earth law framework, an Ocean-centered approach views humanity as interconnected with the Ocean, recognizes societies' collective duty and reciprocal responsibility to protect and conserve the Ocean, and puts aside short-term gain to respect and protect future generations of all life and the Ocean's capacity to regenerate and sustain natural cycles. This Essay presents Ocean-centered governance as an approach to help achieve the 10 challenges for collective impact put forward as part of the UN Decade of Ocean Science for Sustainable Development and therefore living in a harmonious relationship with the Ocean.

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)0.009
metaresearch head score (Gemma)0.009
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 score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.020
Scholarly communication0.0100.010
Open science0.0010.008
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations36
Published2022
Admission routes1
Has abstractyes

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