How can a new UN ocean treaty change the course of capacity building?
Bibliographic record
Abstract
Abstract Few States are able to undertake scientific research in the half of the planet that lies in marine areas beyond national jurisdiction. Capacity building is therefore a key part of the development of a new international legally binding instrument for the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction, under the United Nations Convention on the Law of the Sea (BBNJ Agreement). The final negotiations for the BBNJ Agreement are scheduled for early 2022, after almost two decades of development. There is an urgent need to address remaining questions relating to capacity building to secure an effective and equitable outcome from this process and safeguard the global ocean commons. Persisting gaps in scientific capacity cast doubt on the adequacy of past and current approaches to implement long‐standing international commitments. There is a need to build equitable partnerships for long‐term outcomes. As an international legally binding instrument, the BBNJ Agreement is a critical opportunity to change the course of capacity building by strengthening the international legal framework, including funding, information‐sharing, monitoring and decision‐making. This rapidly closing window to develop international legal obligations, collaboration frameworks and funding mechanisms is relevant not only to the conservation of the global ocean commons, but also for ocean sustainability more generally as the UN Ocean Decade begins.
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.
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 | 0.043 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".