Knowledge co-production: A pathway to effective fisheries management, conservation, and governance
Bibliographic record
Abstract
Abstract Although it is assumed that the outcomes from scientific research inform management and policy, the so-called knowledge–action gap (i.e., the disconnect between scientific knowledge and its application) is a recognition that there are many reasons why new knowledge is not always embraced by knowledge users. The concept of knowledge co-production has gained popularity within the environmental and conservation research communities as a mechanism of bridging the gap between knowledge and action, but has yet to be fully embraced in fisheries research. Here we describe what co-production is, outline its benefits (relative to other approaches to research) and challenges, and provide practical guidance on how to embrace and enact knowledge co-production within fisheries research. Because co-production is an iterative and context-dependent process, there is no single way to do it, but there are best practices that can facilitate the generation of actionable research through respectful and inclusive partnerships. We present several brief case studies where we describe examples of where co-production has worked in practice and the benefits it has accrued. As more members of the fisheries science and management community effectively engage in co-production, it will be important to reflect on the processes and share lessons with others. We submit that co-production has manifold benefits for applied science and should lead to meaningful improvements in fisheries management, conservation, and governance.
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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 | 0.070 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.035 | 0.041 |
| Open science | 0.005 | 0.057 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".