Improving the Economic Management of the Bristol Bay (Alaska) Sockeye Salmon Fishery in the Age of Aquaculture
Bibliographic record
Abstract
When evaluated using biological criteria, Alaskan salmon fisheries are considered to be among the best managed commercial fisheries in the world. Nevertheless, the economic performance of these fisheries has deteriorated in recent years as a result of a long‐term decline in ex‐vessel prices triggered by exponential growth in the world salmon farming sector, with prices reaching historic lows during the late 1990s and early 2000s. Building on the regulated open‐access model for renewable resources developed by Homans and Wilen (1997), Valderrama and Anderson (2010) conducted a formal examination of market interactions between the Bristol Bay sockeye salmon fishery and the world aquaculture sector. Their study demonstrated that the rents extracted during the initial years of the limited‐entry program were gradually dissipated as a result of overcapacity and price declines caused by aquaculture. In this study, the Valderrama and Anderson (2010) econometric model is used as a framework to evaluate the economic benefits of rights‐based management in the Bristol Bay fishery. The analysis illustrates the efficiency gains that management schemes such as harvesting cooperatives could generate by lowering the level of effort in the fishery. Given that growth in world aquaculture is expected to continue in the foreseeable future, improving efficiency in the fisheries sector is an essential step to restore some of the rents lost to competition with the aquaculture sector.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".