Blue Economy and Competing Discourses in International Oceans Governance
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".