Knowledge Co-production in Recreational Fisheries Science and Management
Bibliographic record
Abstract
Abstract Recreational fisheries need flexible approaches to knowledge production and decision support that involve interaction with stakeholders of civil society (e.g., recreational fishers, guides, conservationists), governments, and other organised and non-organised actors. Co-production of knowledge grounds research in relevant societal challenges and yields outputs that can have a transformative impact on practice, management, and governance. The term co-production is variously defined and used in the literature. We consider co-production as a process where research questions are informed by practical problems or co-developed, studies are implemented, and findings are interpreted jointly by scientists and other actors in problem-oriented ways that meet their collective interests and needs, while bridging different knowledge domains and ways of knowing. The concept shares overlap with co-design, co-creation, mode-2 science, transdisciplinary science, action research, citizen and community science, co-learning, co-assessment, and other related “co-terms,” but is not equivalent. In this chapter, we introduce and define co-production and place it in the context of research and management (and contrast it with co-management) related to recreational fisheries, laying out the aspirations, benefits, and challenges. A short narrative review of co-production work in recreational fisheries is provided. We write from a Western academic perspective on the key steps of co-production: identifying which stakeholders to include, exploring how to co-design research, determining data needs and roles, and suggesting one procedural approach. We also refer to leading methodological guidelines. We discuss the challenges and give guidance on weaving diverse visions, world views, and types and sources of knowledge and the importance of being aware of power asymmetries, roles, and (often hidden) norms and values in co-producing knowledge. The chapter concludes with three co-production case studies from recreational fisheries as examples.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".