Knowledge co-production: A pathway to effective fisheries management, conservation, and governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it