Valuing seafood: The Peruvian fisheries sector
Bibliographic record
Abstract
There are tradeoffs in managing fisheries, and ideally such tradeoffs should be known when setting fisheries policies. An aspect of this, which is rarely considered, is the spin-off effect of different fisheries: the economic and social benefits that fisheries generate through processing through distribution and on to the end consumer. This study evaluated the benefits generated in the Peruvian marine fisheries sector through a comprehensive value chain analysis, based on a newly-developed combined ecosystem-economic modeling approach, which was integrated in the widely-used Ecopath with Ecosim approach and software. The value chain was parameterized by extensive data collection through 35 enterprise types covering the marine fisheries sector in Peru, including the world's biggest single-species fishery for anchoveta. While anchoveta is what is known about Peruvian fisheries, the study finds that anchoveta accounts for only 31% of the sector contribution to GDP and for only 23% of the employment. Thus, while anchoveta indeed is the fundamental fish species in the Peruvian ecosystem, there are other fisheries to be considered for management. The study indicates that the economic multipliers for Peruvian fisheries were 2.9 on average over the industry, and that these varied surprisingly little between fleets and between seafood categories indicating that the multipliers can be used beyond Peru to generalize the spin-off effect of the value chain. Employment multipliers vary much more across types of fisheries, but also around an average of 2.9; here it was clear that longer value chains result in more employment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".