Challenges and Prospects of Fisheries Co-Management under a Marine Extractive Reserve Framework in Northeastern Brazil
Bibliographic record
Abstract
In Brazil, Marine Extractive Reserves—MERs (Reservas Extrativistas Marinhas) represent the most significant government-supported effort to protect the common property resources upon which traditional small-scale fishers depend. From an initial small-scale experience in 1992, MERs have expanded countrywide, now encompassing 30 units (9,700 km2) and nearly 60,000 fishers. Despite such escalating interest in the model, there is little research on the effectiveness of MERs. In this article, we discuss relevant parts of the history and examine the current situation of the fisheries co-management initiative in the Marine Extractive Reserve of Corumbau, which was created in 2000 as the first MER to encompass coral reefs and reef fisheries. We describe the Extractive Reserve co-management arrangement and its main policy and legislative challenges. Finally, we discuss the prospects for the use of MERs as management frameworks for traditional small-scale fisheries in Brazil.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".