The future of ocean governance
Bibliographic record
Abstract
Ocean governance is complex and influenced by multiple drivers and actors with different worldviews and goals. While governance encompasses many elements, in this paper we focus on the processes that operate within and between states, civil society and local communities, and the market, including industry. Specifically, in this paper, we address the question of how to move towards more sustainable ocean governance aligning with the sustainable development goals and the UN Ocean Decade. We address three major risks to oceans that arise from governance-related issues: 1) the impacts of the overexploitation of marine resources; 2) inequitable distribution of access to and benefits from marine ecosystem services, and 3) inadequate or inappropriate adaptation to changing ocean conditions. The SDGs have been used as an underlying framework to develop these risks. We identify five drivers that may determine how ocean governance evolves, namely formal rules and institutions, evidence and knowledge-based decision-making, legitimacy of decision-making institutions, stakeholder engagement and participation, and empowering communities. These drivers were used to define two alternative futures by 2030: (a) ‘Business as Usual’ - a continuation of current trajectories and (b) ‘More Sustainable Future’ – optimistic, transformational, but technically achievable. We then identify what actions, as structured processes, can reduce the three major governance-related risks and lead to the More Sustainable Future. These actions relate to the process of co-creation and implementation of improved, comprehensive, and integrated management plans, enhancement of decision-making processes, and better anticipation and consideration of ambiguity and uncertainty.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".