Stakeholder Engagement as a Core Component of Recreational Marine Fisheries Research, Education, and Conservation
Bibliographic record
Abstract
Abstract Effective modern conservation depends on active stakeholder participation. Although stakeholder engagement is increasing, the extent of this engagement and the successful application of outcomes to science and management varies regionally and among types of fisheries. A collaborative model that emphasizes knowledge coproduction with stakeholders better identifies research needs and conservation threats, and influences research and policy outcomes. Stakeholder integration can be facilitated by nongovernment organizations, such as boundary organizations. Bonefish and Tarpon Trust is a science-based, conservation organization founded in 1998 by recreational fishers and fishing guides that focuses on marine recreational fisheries in the Caribbean Sea and western North Atlantic Ocean. The Trust engages fishers directly, incorporating their knowledge and perspectives to identify conservation concerns, shape research, contribute to data collection, and disseminate information, and work with resource managers and scientific researchers to address conservation and management needs. This approach is demonstrated in case studies that show integration of recreational fishers in science, assessment of conservation threats, and application of findings to management for the recreational flats fishery in the Caribbean Sea and western North Atlantic Ocean, in the context of broader efforts of stakeholder collaboration toward actionable science to inform management.
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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 | 0.043 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".