Small-scale fisheries in ecologically sensitive areas in Latin America and the Caribbean: Do marine protected areas benefit fisheries governance?
Bibliographic record
Abstract
Many small-scale fisheries (SSFs) in Latin America and the Caribbean (LAC) operate in ecologically sensitive areas, where balancing conservation and resource use is challenging. 'Multiple-use' marine protected areas (MPAs) have been implemented to accommodate SSFs (among other uses) within areas designated for conservation, creating opportunities and challenges for SSF governance. We analyzed eleven case studies from LAC to explore: (1) how different MPA institutional designs affect key aspects of SSF governance and (2) the links between these effects and the type of initiative that promoted MPA establishment (origin). Results indicate that the existence of an MPA benefited SSF governance in many ways, with more pronounced positive effects in MPAs with mixed to bottom-up origin supported by well-organized fishing groups. In addition, the presence of supportive MPA authorities that leveraged local capacities and initiatives and adopted flexible and collaborative governance systems made a difference in several cases. Lessons for integrating MPA and SSF governance are drawn.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".