Editorial: Co-creating knowledge with fishers: challenges and lessons for integrating fishers’ knowledge contributions into marine science in well-developed scientific advisory systems
Bibliographic record
Abstract
Co-creating knowledge with fishers: challenges and lessons for integrating fishers' knowledge contributions into marine science in well-developed scientific advisory systems This Research Topic on 'Co-creating knowledge with fishersintegrating fishers' knowledge contributions into marine science' brings together 16 papers from researchers and fishers who have been leading science-industry research collaboration (SIRC) across regions with well-developed scientific advisory systems.In such systems, marine science is heavily dependent on both fisheries-independent and fisheries-dependent data from statutory obligations (e.g., catch and effort data).Knowledge gaps could be addressed more fully by gathering, accessing and integrating fishers' observational and experiential knowledge.Whilst efforts to this end are gaining momentum, there are few documented examples where SIRC projects are shown to be effective in scientific assessments and to inform advisory processes.Challenges associated with integrating fishers' knowledge contributions relate to both the mechanics of the scientific advisory system and opinions on governing its integrity.Deliberate contributions from industry to science, for example through SIRC, are frequently met with questions around conflict of interest, trustworthiness and reliability, hindering their integration into/with science in support of management.This is problematic in a science-policy context where use of best available Frontiers in Marine Science frontiersin.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".