Participation in planning and social networks increase social monitoring in community‐based conservation
Bibliographic record
Abstract
Abstract Biodiversity conservation is often limited by inadequate investments in monitoring and enforcement. However, monitoring and enforcement problems may be overcome by encouraging resource users to develop, endorse, and subsequently enforce conservation regulations. In this article, we draw upon the literature on common‐pool resources and social networks to assess the impacts of participation and network ties on the decisions of fishers to voluntarily report rule violations in two Jamaican marine reserves. Data was collected using questionnaires administered through personal interviews with fishers ( n = 277). The results suggest that local fishers are more likely to report illegal fishing if they had participated in conservation planning and if they are directly linked to community‐based wardens in information sharing networks. This research extends well‐established findings regarding the role and impacts of participation on biodiversity conservation by highlighting the importance of synergies between participation and social networks for voluntary monitoring of conservation regulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".