Addressing Marine and Coastal Governance Conflicts at the Interface of Multiple Sectors and Jurisdictions
Bibliographic record
Abstract
Marine and coastal activities are closely interrelated, and conflicts among different sectors can undermine management and conservation objectives. Governance systems for fisheries, power generation, irrigation, aquaculture, marine biodiversity conservation, and other coastal and maritime activities are typically organized to manage conflicts within sectors, rather than across them. Based on the discussions around eight case studies presented at a workshop held in Brest in June 2019, this paper explores institutional approaches to move beyond managing conflicts within a sector. We primarily focus on cases where the groups and sectors involved are heterogeneous in terms of: the jurisdiction they fall under; their objectives; and the way they value ecosystem services. The paper first presents a synthesis of frameworks for understanding and managing cross-sectoral governance conflicts, drawing from social and natural sciences. We highlight commonalities but also conceptual differences across disciplines to address these issues. We then propose a novel analytical framework which we used to evaluate the eight case studies. Based on the main lessons learned from case studies, we then discuss the feasibility and key determinants of stakeholder collaboration as well as compensation and incentive schemes. The discussion concludes with future research needs to support policy development and inform integrated institutional regimes that consider the diversity of stakeholder interests and the potential benefits of cross-sectoral coordination.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".