The cost of fisheries management
Bibliographic record
Abstract
Theory: The costs of fisheries management - who should pay?, Ragnar Arnason, Rognvaldur Hannesson and William E. Schrank Fisheries management costs - some theoretical implications, Ragnar Arnason Financing fishery management - principles and economic implications, Peder Andersen and Jon G. Sutinen. Country Studies: Management and enforcement costs in Norway's fisheries, Rognvaldur Hannesson The cost of marine fishery management in Eastern Canada - Newfoundland, 1989/1990 to 1999/2000, William E. Schrank and Blanca Skoda Government expenditures on fisheries and fisheries management in Iceland, Ragnar Arnason A comparison of fisheries management costs in Iceland, Norway and Newfoundland, Ragnar Arnason, Rognvaldur Hannesson and William E. Schrank Fishery management costs and rent extraction - the case of Namibia, Vilhjalmur H. Wiium and Aina S. Uulenga Fisheries management costs in Thai marine fisheries, Rolf Willmann, Pongpat Boonchuwong and Somying Piumsombun Fisheries management costs - concepts and studies, Paul Wallis and Ola Flaaten. Cost Recovery: The effects of unilateral cost recovery in an international fishery, Sean Pascoe, Simon Mardle and Aaron Hatcher Cost recovery in fisheries management - the Australian experience, Anthony Cox Cost recovery in fisheries management - the New Zealand experience, Nick Wyatt. Conclusions: Fisheries management costs - findings and challenges for future research, Jon G. Sutinen and Peder Andersen.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".