The Quilt of Sustainable Ocean Governance: Patterns for Practitioners
Bibliographic record
Abstract
In recent decades, scientists and practitioners have increasingly focused on identifying and codifying the best ways to manage activities in marine systems, leading to the development and implementation of concepts such as the social-ecological systems approach, ecosystem-based management, integrated management, marine spatial planning, participatory co-management, and the precautionary approach. To date, these concepts appear as separate entities: they have parallel literature streams; have been applied most often individually in attempts to improve governance and management; and in many ways, seem to be competing for attention. This patchwork of approaches may be hindering effective ocean governance. We propose that desirable features from these frameworks could be woven together to form the basis of more effective and equitable ocean governance arrangements across contexts, sectors, and scales. This article synthesizes the efforts of an IMBeR (Integrated Marine Biosphere Research Project) conference session and working group, that brought together experts in these diverse concepts with the objective of producing a synthesis of how they could be more effectively integrated for improved ocean sustainability outcomes. We reviewed and compared the concepts in terms of (a) the need to achieve a comprehensive suite of sustainability objectives, (b) similarities and differences in their scope, and (c) their place in practical management, policy and regulation. Achieving greater cross-sectoral integration, or a more holistic perspective on management for sustainability is at the core of each concept. All deal with aspects of governance and most, with improved participation in governance. The major differences in the origin and historical application of each concept are reflected in the degree of implicit or explicit focus given to different objectives of sustainability. Overall, the concepts are especially strong for ecological and institutional or governance considerations, moderately strong for economic aspects, and weakest for the social-cultural pillar of full spectrum sustainability. There is no panacea, and no emergent hierarchy among concepts. Some concepts fit better with top-down legislation-based efforts, others with more bottom-up stakeholder driven efforts. The selection of the core concepts for a situation will depend in a large part on which concepts are specified, or demand focus, in the legal and policy context of the situation (or area) of interest. No matter how influential or dominant a single concept might be, pragmatically, different concepts will be used in different areas, and there may always be the need for a combination of concepts and objectives woven together to achieve a cohesive quilt of sustainability.
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 | 0.052 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.010 | 0.014 |
| 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".