Illustrating the critical role of human dimensions research for understanding and managing recreational fisheries within a social‐ecological system framework
Bibliographic record
Abstract
Abstract Effective management of recreational fishing requires understanding fishers and their actions. These actions constitute critical links between social and ecological systems that result in outcomes that feedback and influence recreational fishers' actions and the management of these actions. Although much research exists on recreational fishers and their actions, this research is often disconnected from management issues. One way to help to overcome this disconnect is to illustrate how past research on the social component of recreational fishing fits within an emerging coupled social‐ecological system ( SES ) framework. Herein, a conceptual SES is first developed with specific attention to recreational fisheries. This SES is then used to illustrate the importance of considering human dimensions research for articulating, studying and ultimately managing key outcomes of recreational fisheries (e.g. fish population conservation, fisher well‐being) using the example of harvest regulations and a brief review of past interdisciplinary research on recreational fishing. The article ends by identifying key research needs including understanding: how factors such as management rules affect the diversity of actions by recreational fishers; how governance and management approaches adapt to changing social and resource conditions; and how recreational fishers learn and share information.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| 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".