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Record W4416949594 · doi:10.1038/s44183-025-00165-y

Hidden costs and propped-up profits: unraveling the economics of Europe’s purse-seine tuna fishing industry

2025· article· en· W4416949594 on OpenAlex
Théophile Froment, Frédéric Le Manach, Liam Campling, Daniel J. Skerritt, Arne Kinds

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenpj Ocean Sustainability · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersAgence Française de DéveloppementWaterloo FoundationOak Foundation
KeywordsFishingSubsidyTunaFishing industryEconomic impact analysisEconomic stability

Abstract

fetched live from OpenAlex

Despite tuna fisheries’ global economic significance and contributions to food security and trade, we find that without substantial subsidies, notably fuel tax exemptions and fishing access agreement fees supported by public funds from the European Union, the European purse seine tuna industry would be highly unprofitable. Between 2010 and 2023, EUR 2.1 B in subsidies were funnelled into the sector (EUR 179.4 M from fishing access agreements, and EUR 1.9 B from fuel tax concessions), masking underlying financial issues and commercial non-viability. French companies reported continuous losses in recent years, even with these subsidies. Spanish firms performed better, likely due to complex ownership structures that facilitate tax optimization strategies, lower operating costs, and increased access to fishing grounds and seafood markets. Additionally, the sector’s economic survival appears to come at the expense of working conditions and pay, environmental sustainability, and legal compliance, including wage suppression and fishing violations. This research highlights the urgent need for policy reform in the European tuna fishing industry, addressing the reliance on harmful subsidies and the intensification of labor exploitation as a means to avoid economic unviability. The abolishment of harmful subsidies, sustainable fishing practices, and fairer employment conditions must be integrated to prevent a looming crisis that threatens ecological systems, economic stability and livelihoods.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.256
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it