Dissecting co‐management: Fisher participation across management components and implications for governance
Bibliographic record
Abstract
Abstract Co‐management—fisher participation in fisheries management—varies across two fundamental dimensions. The most commonly addressed is a ‘ladder of participation’ reflecting the degree to which decision‐making is shared between government and fishers. The other dimension reflects if, and how, co‐management is implemented across the ‘management spectrum’ of functional components of management: (1) direction‐setting, planning and policy development; (2) harvest management; (3) compliance and enforcement; (4) ecosystem stewardship, conservation, rehabilitation; (5) research; and (6) organizational management and development. This article presents an approach to combining these two dimensions in a comprehensive manner, to better understand and assess the nuances of co‐management in practice. The approach is tested through application to fisheries of Nova Scotia, Canada, with representatives of organized fisher associations assessing the nature and extent of participation, both current and desired, for each of the six management components. This leads to insights about the fishery management components that tend to have greater or lesser fisher participation, differences in perceived levels of current and desired participation, the willingness and capacity of fisher associations to take on various management tasks and potential directions to improve fishery co‐management practices.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".