Communication between scientists, fishery managers and recreational fishers: lessons learned from a comparative analysis of international case studies
Bibliographic record
Abstract
Abstract The management of recreational fisheries benefits from good collaboration between scientists, managers and recreational fishers. However, the level of collaboration largely depends on the levels of effective communication among the different stakeholders. This paper presents the views of scientists, managers and fishers concerning the quality of communication in eleven case studies of recreational fisheries. Case studies were synthesised and common reasons why communication did not always flow as intended were identified. The prevalent barriers to good communication, and therefore collaboration included a lack of rigorous scientific information transfer from scientists to fishers and managers, a fear from fishers that management actions will limit fishing opportunities, pre‐existing antagonism between commercial and recreational fisheries, and fishers' suspicion of science. Overcoming these issues is paramount to improve collaboration and participatory processes that help lead to robust, well‐accepted management actions.
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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 | 0.041 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".