Diversity, equity, and inclusion in the Blue Economy: Why they matter and how do we achieve them?
Bibliographic record
Abstract
The Blue Economy (BE) has captured the attention of diverse interests to the ocean and there is rising concern about making it more equitable and inclusive. As it currently stands, diversity, social equity, and inclusion considerations have not been foregrounded in the discourse surrounding the BE and are continuously overlooked and undervalued. This paper reviews the ongoing social inequalities in the BE and distribution of benefits and costs across different groups in society. It also explores why equity matters, and how it can be achieved. Mirroring the call for under-represented or marginalized social groups to receive a fair share of the returns, which may be more than they have received to date. Our analysis shows that between 1988 and 2017, a Germany–based company has registered about 39% of all known marine genetic resources, while three companies in Asia control 30% of the market share of seafood sector in 2018. These findings show high consolidation of the ocean space by top corporations. Therefore, this paper argues that the exclusion of equity considerations within the BE investments can undermine ocean-based activities such as marine wildlife conservation initiatives that may disrupt the ocean sustainability agenda.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".