Improving marine protected area governance through collaboration and co-production
Bibliographic record
Abstract
Marine protected areas (MPAs) socio-ecological effectiveness depends on a number of management and governance elements, among which stakeholder engagement and community support play key roles. Collaborative conservation initiatives that engage stakeholders in action research and knowledge co-production processes can enhance management and governance of MPAs. To design effective strategies aimed at reconciling biodiversity conservation and management of sustainable human uses, it is key to assess how local communities respond to such initiatives and identify the set of contextual factors, institutional, local and individual, potentially affecting these responses. This paper presents the approach and results of one such initiative, spanning 6 EU countries and 11 MPAs in the Mediterranean Sea, focusing on small-scale fishers as key MPA users. Through a collaborative project, managers and fishers agreed upon specific governance interventions (e.g. increasing stakeholder engagement, engaging fishers in monitoring activities, reducing fishing efforts) to be implemented in each MPA for one year. Structured surveys queried: MPA managers on the MPA context, governance structure, feasibility and effectiveness of the tested interventions; and small-scale fishers on their perceptions of the impact of the tested interventions on a set of 9 socio-ecological variables (e.g. amount of fish caught, level of participation in decision-making, support for the MPA). Results revealed that the interventions tested were relatively feasible, effective and cost-effective. Fishers reported positive perceptions of the interventions for the 9 variables considered, especially for level of support for the MPA and for those associated with aspects of governance. Proportional odds models highlighted perceived effects are maximized under certain institutional, local and individual circumstances (e.g. old MPAs, small fisher communities, and fishers with a high proportion of income from fisheries). Findings highlight that employing good governance processes involving stakeholders may rapidly generate improved local support for conservation and provide insights for potential leverage points upon which to act to maximize perceived effectiveness and enhance support toward MPAs.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".