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Record W2326387144 · doi:10.1177/1070496515580797

Blue Economy and Competing Discourses in International Oceans Governance

2015· article· en· W2326387144 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Environment & Development · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCorporate governanceLivelihoodEconomyNatural capitalEnvironmental governancePolitical scienceCapital (architecture)Economic systemEconomicsGeographyEcologyEcosystem

Abstract

fetched live from OpenAlex

In this article, we track a relatively new term in global environmental governance: “blue economy.” Analyzing preparatory documentation and data collected at the 2012 UN Conference on Sustainable Development (i.e., Rio + 20), we show how the term entered into use and how it was articulated within four competing discourses regarding human–ocean relations: (a) oceans as natural capital, (b) oceans as good business, (c) oceans as integral to Pacific Small Island Developing States, and (d) oceans as small-scale fisheries livelihoods. Blue economy was consistently invoked to connect oceans with Rio + 20’s “green economy” theme; however, different actors worked to further define the term in ways that prioritized particular oceans problems, solutions, and participants. It is not clear whether blue economy will eventually be understood singularly or as the domain of a particular actor or discourse. We explore possibilities as well as discuss discourse in global environmental governance as powerful and precarious.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.196
Teacher spread0.187 · 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