Integrating fishers’ knowledge research in science and management
Bibliographic record
Abstract
Abstract Fishers' knowledge research (FKR) aims to enhance the use of experiential knowledge of fish harvesters in fisheries research, assessment, and management. Fishery participants are able to provide unique knowledge, and that knowledge forms an important part of “best available information” for fisheries science and management. Fishers' knowledge includes, but is much greater than, basic biological fishery information. It includes ecological, economic, social, and institutional knowledge, as well as experience and critical analysis of experiential knowledge. We suggest that FKR, which may in the past have been defined quite narrowly, be defined more broadly to include both fishery observations and fishers “experiential knowledge” provided across a spectrum of arrangements of fisher participation. FKR is part of the new and different information required in evolving “ecosystem-based” and “integrated” management approaches. FKR is a necessary element in the integration of ecological, economic, social, and institutional considerations of future management. Fishers' knowledge may be added to traditional assessment with appropriate analysis and explicit recognition of the intended use of the information, but fishers' knowledge is best implemented in a participatory process designed to receive and use it. Co-generation of knowledge in appropriately designed processes facilitates development and use of fishers' knowledge and facilitates the participation of fishers in assessment and management, and is suggested as best practice in improved fisheries 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 | 0.046 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".