Towards adaptive co-management of small-scale fisheries in Uruguay and Brazil: lessons from using Ostrom’s design principles
Bibliographic record
Abstract
The literature on commons has established the validity and significance of Elinor Ostrom’s design principles for collective action. Can these principles be used to guide policies and initiatives towards adaptive co-management? We analyze this idea by using two case studies, Piriápolis (Uruguay) and Paraty (Brazil). Both cases are small-scale fisheries, and both have been experiencing a social-ecological crisis in a context of prevailing top-down government management. However, there are signs that government policies are moving towards participatory governance. The objective of this article is to identify opportunities and barriers to adaptive co-management of small-scale fisheries in Uruguay and Brazil using Ostrom’s design principles for guidance. Both case studies partially meet seven of the eleven design principles (as amended by Cox and colleagues), but do not fulfill four. The analysis of the fisheries using Ostrom’s principles sheds light on the opportunities and barriers to adaptive co-management in three categories: resource system, resource users, and governance system. Barriers include long-standing conflicts between small-scale fishers and government agencies, and between small and large-scale fisheries sectors. Nevertheless, recent initiatives involving participatory approaches to research and management show potential to improve compliance with several principles. Two weaknesses of using Ostrom’s principles for the analysis of the cases were a lack of attention to social learning and the exclusion of external drivers.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".