Considering Indigenous Peoples and local communities in governance of the global ocean commons
Bibliographic record
Abstract
The United Nations are currently negotiating a new international legally-binding instrument to govern the global ocean commons, a vast area beyond national jurisdiction (ABNJ) owned by everyone but not cared for by any single entity. Indigenous Peoples and local communities (IPLCs) have been underrepresented in the debate about governance of ABNJ despite their internationally recognized rights and their role as custodians of many globally-significant migratory species that travel between coasts and high seas. Here we use examples of active transboundary connectivity by migratory species as case studies to highlight the relevance of IPLCs on islands, coasts and beyond to the governance of the global ocean commons, and make a case for their essential and beneficial inclusion in it. Many migratory species are culturally and economically important to IPLCs, who are frequently the first to suffer if these species are overexploited or decline due to inadequate management in ABNJ. Four case studies (Pacific salmon, Marine Turtles, Black-footed and Laysan Albatrosses, Northern fur seal) illustrate knowledge, innovations and practices of IPLCs that have global importance in informing strategies for conservation, sustainable and equitable use of marine species in general. IPLCs can contribute to enriching the diversity of approaches and solutions, and by elaborating on principles directly relevant for governance of ABNJ within the UN process and beyond.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".