Engaging the tropical majority to make ocean governance and science more equitable and effective
Bibliographic record
Abstract
How can ocean governance and science be made more equitable and effective? The majority of the world’s ocean-dependent people live in low to middle-income countries in the tropics (i.e., the ‘tropical majority’). Yet the ocean governance agenda is set largely on the basis of scientific knowledge, funding, and institutions from high-income nations in temperate zones. These externally driven approaches undermine the equity and effectiveness of current solutions and hinder leadership by the tropical majority, who are well positioned to activate evidence-based and context-specific solutions to ocean-sustainability challenges. Here, we draw together diverse perspectives from the tropics to propose four actions for transformational change that are grounded in perspectives, experiences, and knowledge from the tropics: 1. Center equity in ocean governance, 2. Reconnect people and the ocean, 3. Redefine ocean literacy, and 4. Decolonize ocean research. These actions are critical to ensuring a leading role for the tropical majority in maintaining thriving ocean societies and ecosystems.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".