Diversity, equity, and inclusion in the Blue Economy: Why they matter and how do we achieve them?
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.259 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it