Local Stakeholders Understand Recreational Fisheries as Social-Ecological Systems but Do Not View Governance Systems as Influential for System Dynamics
Bibliographic record
Abstract
Recognition that there are often social and ecological components to problems that arise from management of shared resources has led to a dominant paradigm among academics that natural resource management should consider coupled social-ecological systems. For academic theory to have real-world impact it must be understood and acted upon by stakeholders at a local scale. However, it is unclear if stakeholders view their systems as coupled social-ecological systems. We interviewed key stakeholders in an inland recreational fishery to solicit their mental models of system dynamics in the context of Ostrom‘s Social-Ecological Systems Framework (SESF). We found that stakeholders in aggregate considered all components of the SESF (actors, resource systems, environmental settings, and governance systems) in their view of recreational fisheries. However, researchers viewed governance system and environmental setting components as less diverse than actor and resource system components, while anglers and managers viewed the actor component as more diverse than all other components. In addition, all stakeholders viewed governance system and environmental setting components as less influential than actor and resource system components. Given strong empirical evidence of positive relationships between the number and diversity of governance system attributes and successful fisheries outcomes, our results suggest that governance systems that prevent free riding, enforce rules through graduated sanctions, and address large scale problems at the local scale through nested institutions could improve social-ecological outcomes in inland recreational fisheries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".