Collaborative networks for collective action in a Brazilian Marine Extractive Reserve
Bibliographic record
Abstract
Community-based co-management strategy has been implemented in coastal and marine protected areas to reconcile resource use with biodiversity conservation, and to foster governance through the participation of multiple actors like governments, social civil organizations, and traditional resource users. How actors engage in collaboration will determine specific network structures that can facilitate or hinder different processes. The analysis of network structures can evidence the presence of social capital and leadership, both necessary to achieve collective action and contribute to build resilience and increase adaptability. Through the statement of collective action problems related to (1) biodiversity, (2) governance, and (3) socioeconomic issues we study the potential for collaboration between institutions in the Deliberative Council of Canavieiras Extractive Reserve. We identify network structures that can promote the presence of social capital and leadership necessary to address the collective action problems that may arise. The federal environmental agency was the most sought institution for solving almost all problems. This central institution can act as a coordinator and fosters collective action. Regardless, the high dependency on this federal environmental agency can affect the system’s resilience because of its complex and bureaucratic structure, which can delay and hinder the collective action process. Traditional communities and their leadership institution have high social capital for collective action. Several institutions seem to share the bridging position in the networks, revealing the decentralization of this role that may provide resilience to changes in the governance of the system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".