Unveiling the hidden hands: Analysis of corporate ownership of industrial tuna fishing vessels in the Eastern Pacific Ocean
Bibliographic record
Abstract
Distant-water fishing (DWF) refers to fishing operations conducted by companies in waters beyond their national exclusive economic zones (EEZs), often targeting the EEZs of other coastal states or international waters. Research on DWF typically emphasizes the flag states of vessels, rather than the nationalities of the corporations that own them, despite evidence of efforts to obscure ownership. This paper examines the corporate owners of 1648 industrial and semi-industrial tuna fishing vessels in the Eastern Pacific Ocean (EPO) using Orbis data, analyzing ownership at three levels: flag state, direct corporate owners, and ultimate corporate owners. Results show that flag state data alone understate the true fishing capacity of key DWF nations. Shifts in ownership nationality across levels are significant, notably for Taiwanese and Spanish corporations owning vessels through proxies in the Global South. These ownership shifts impact 6 % of vessels and 14 % of gross tonnage in the registered EPO tuna fleet. Furthermore, spatial analysis using Global Fishing Watch data highlights the Galapagos Islands EEZ as a critical fishing zone, frequently accessed by foreign fishing corporations through Ecuadorian intermediaries. This study underscores the need to incorporate ownership data into fisheries governance for greater transparency and accountability. The systematic collection and analysis of ownership data would allow fisheries managers to better monitor capacity, address power concentration, and promote policies that ensure fairer distribution of resources and benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".