Architecture and agency for equity in areas beyond national jurisdiction
Bibliographic record
Abstract
The United Nations (UN) Sustainable Development Goals (SDGs) and the UN Decade of Ocean Science for Sustainable Development (Ocean Decade) bring increased attention to various aspects of ocean governance, including equity. One of the Ocean Decade's identified challenges is to develop a sustainable and equitable ocean economy, but questions arise about how to conceptualize the multiple dimensions of equity in an ocean context. These questions become more complex as activities move away from coastal ecosystems and communities into off-shore Areas Beyond National Jurisdiction (ABNJ), where ocean resources are recognized simultaneously as unowned/open access and as common heritage. In this paper, we mobilize the Earth System Governance analytics of ‘architecture’ and ‘agency’, to reflect on the possibilities for equity in ABNJ. Motivated by the general attention to equity in UN initiatives like the SDGs and the Ocean Decade, we describe current UN architecture for ocean governance, including principles that might support equity. Existing UN architecture focuses on distributional equity among nation states, with less attention to recognitional or procedural equity. State actors have most agency, while non-state actors can exercise some via broad UN declarations and through mechanisms like ‘major groups.’ We use on-going negotiations in the International Seabed Authority on rules for mineral exploitation and in the Intergovernmental Conference on an international legally binding instrument under UNCLOS on the conservation and sustainable use of marine biological diversity of Areas Beyond National Jurisdiction to illustrate how existing architecture shapes possibilities for equity in ABNJ. As new governance possibilities are imagined, attending to existing architecture and agency can help avoid further entrenching existing power imbalances and unwittingly reproducing or exacerbating inequities.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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".