Fishery Co-management: A Practical Handbook
Bibliographic record
Abstract
For many years, Canada's International Development Research Centre (IDRC) has maintained an active portfolio of projects examining co-management and community-based management in fisheries and other resource systems. Since the publication of Managing Small-scale Fisheries (Berkes et al., 2001), there has been an increasing demand for guidance on what IDRC has learned about co-management, particularly across different geographical settings, socio-economic conditions, and histories of operation; and how it could apply to other types of fishing, link to other livelihoods, relate to other dynamic processes (such as the migration of fishermen), and respond to the seasonal nature of fish resources. This book attempts to respond to this demand by compiling recent experience from as wide a cross section of research as possible. During the development of this book, both IDRC and the authors wrestled with the concept of co-management. Given the evolving nature of this science, for example, what does co-management cover and how widely is the concept accepted? Importantly, there has been increasing acceptance of the idea that co-management is not an end point but rather a process -- a process of adaptive learning. Recognizing the diversity of both local contexts (ecological and social) and factors depleting the fishery (such as overfishing and habitat destruction), however, would it even be possible to put together a book of lessons learned? As you will soon discover, IDRC and the authors felt that it was neither possible nor desirable to produce a blueprint for fishery co-management. Rather, we agreed that it would be more useful to document the co-management process, as undertaken by both IDRC partners and others, and to put this experience into a form that could be shared with anyone interested in learning more about co-management and what others have learned. This shared and adaptive approach to learning is what this book is all about. In the pages that follow, you will find a complete picture of the co-management process: strengths, weaknesses, methods, activities, checklists and so on.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.020 |
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".