Rethinking blue economy governance – A blue economy equity model as an approach to operationalise equity
Bibliographic record
Abstract
The blue economy was originally conceptualised as having a strong focus on social equity; however, in practice, these equity considerations have been overshadowed by neo-liberal capitalist agendas, which have become dominant in blue economy discourse. A continued expansion of ocean industry developments and activities has resulted in an inequitable share of the burdens and benefits of utilising ocean spaces and has exacerbated wealth disparities and power asymmetries. Therefore, finding mechanisms to reinstate equity as fundamental to blue economy governance and practice is increasingly important. However, there remain few practical examples that outline how to embed equity within blue economy governance and current frameworks for understanding equity are complex, often divergent and less focused on implementation. This paper outlines a new model for conceptualising equity that is clear and easily understood, captures equity’s key components and dimensions, and covers key ethical concerns that arise in blue economy development. Furthermore, this model can be practically applied and embedded into governance structures. To demonstrate the model’s application, the paper outlines one participatory approach to implementing the model in blue economy governance.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".