Tuna trade‐offs: Balancing profit and social benefits in one of the world’s largest fisheries
Bibliographic record
Abstract
Abstract The western and central Pacific Ocean (WCPO) tuna fishery is one of the world's largest in terms of both catch volume and value, providing over half of global tuna catch with a landed value of US $5.84 billion in 2017. Fishing is conducted by both large‐ and small‐scale fleets, with fisheries subsidies disproportionately benefiting the former. The primary objective of this study was to determine the optimal distribution of effort between two large‐scale fisheries (LSF) and two small‐scale fisheries (SSF) in the WCPO under three scenarios: to maximize industry benefits, minimize subsidization or maximize food supply. The objective was approached using a bioeconomic game‐theoretic model. Results indicate opposite distributions of effort to maximize industry benefits (all fishing conducted by LSF) or to minimize subsidization (all fishing by SSF), with more balanced effort distributions to maximize food supply. Total value of capacity‐enhancing subsidies in optimal scenarios ranged from $1.4 billion when industry benefits were maximized to $0.2 billion when subsidization was minimized. Investigation of suboptimal scenarios reveals the flexibility of these results, with wide ranges in outputted state variables for a given goal. Difficulty was encountered in modelling the SSF sector due to data deficiencies, a well‐recognized issue in managing SSF. Investments towards “data equity” to help ensure that management decision‐making can properly account for the SSF sector would be useful. This study has implications for the objectives we set in fisheries management, and the potential trade‐offs, often value‐driven in nature, that we must make explicit in that management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".