Bibliographic record
Abstract
In recent years concern over the mismanagement and depletion of our natural resources has grown.Innovative responses to this trend have been developed in the management of fisheries when groups or communities of fishermen and various levels of government in Canada and the United States have worked out agreements to share decision-making.This book is the first to consolidate information on the different routes by which these co-operative management arrangements have evolved.The authors include anthropologists, environmental planners, biologists, economists, fishery managers, and tribal and governmental leaders.Their contributions examine the process of achieving co-management, the institutions created by co-management arrangements, and the benefits which result.Some of these benefits include more efficient and equitable management, less conflict between government and fishermen, and better co-operation between groups of fishermen.As the cost of centralized government rises and as resource-dependent regions demand greater control over development, co-operative management will become one of the most important means of regulating the use of certain natural resources.Co-Operative Management of Local Fisheries looks at successes and failures of these arrangements for shared decision-making and offers some guidelines for viable co-operative management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.894 | 0.845 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".