Bibliographic record
Abstract
The authors of this volume make three unusual contributions in the process of addressing issues of co-operative fisheries management.The first is that, although they are from disparate disciplines and backgrounds, they focus on the same problems and do so in ways comprehensible to each other.This has been possible partly because many of the authors have training in or exposure to more than one field or function: these include anthropology, biology, economics, environmental science, law, planning, mediation, Native statesmanship, and civil service.Their efforts are in some sense collaborative, in that nearly all participated in a 1986 conference on fisheries comanagement at the University of British Columbia School of Community and Regional Planning.Many authors served as discussants at the conference, and/or later as critics of one another's papers.In this book three of the authors collaborate on the last chapter, which combines the perspectives of three disciplines in asking how far we have come in theory and in practice on the question of co-management.The second way this book is unusual is that most authors are concerned with both theory and practice, and especially with the relationship between the two.The majority of authors have been directly involved in an effort to create viable co-management; the remainder have been long-term close observers.This has created a book in which every description suggests a hypothesis about how co-management is successfully created and/or maintained.As Professor Maurice Stein notes, "one good description is worth ten theories"; this book contains both many good descriptions and many middle-range theoretical propositions about how and why comanagement happens.Reflecting the fertile ground of inter-disciplinary research and dialogue, these propositions are a subset of common property resource management theory and also address real world issues in sustainable community and regional development, and in workplace democracy.The third way this book is unusual is that authors have made an attempt to avoid the specialized jargon of their disciplines and to communicate with a broad readership.They share a belief that we can no longer afford to tackle these intractable problems in isolation from one another.All efforts are needed.All examples add something to our understanding.The making of IX
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.483 | 0.306 |
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".