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Living on the Margin in the Anthropocene: engagement arenas for sustainability research and action at the ocean–land interface

2015· article· en· W967313405 on OpenAlexafffund
Bruce Glavovic, Karin E. Limburg, Kon‐Kee Liu, Helmuth Thomas, Hartwig Kremer, Bernard Avril, Jianke Zhang, Margaret R. Mulholland, Marion Glaser, Dennis P. Swaney

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsDalhousie University
FundersDalhousie UniversityCanada Excellence Research Chairs, Government of Canada
KeywordsAnthropoceneSustainabilityMargin (machine learning)RealmTransformative learningCorporate governancePlanetary boundariesEnvironmental resource managementFrontierEnvironmental planningSustainable developmentBusinessPolitical scienceEnvironmental ethicsSociologyGeographyEcologyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The advent of the Anthropocene underscores the need to develop and implement transformative governance strategies that safeguard the Earth's life-support systems, most critically at the ocean–land interface — the Margin. The seaward realm of the Margin is the new frontier for resource exploitation and colonization to meet the needs of coastal nations and humanity overall. Here, we spotlight the pivotal role of the Margin for planetary resilience and sustainability, highlight priority issues, and outline a research strategy which aims to: (a) better understand Margin social-ecological systems; (b) guide sustainable development of Margin resources; (c) design governance regimes to reverse unsustainable practices; (d) facilitate equitable sharing of Margin resources; and (e) evaluate alternative research approaches and partnerships that address major Margin challenges.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.001
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 head0.121
GPT teacher head0.393
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations56
Published2015
Admission routes2
Has abstractyes

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