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Record W2025641593 · doi:10.1371/journal.pone.0118351

Euros vs. Yuan: Comparing European and Chinese Fishing Access in West Africa

2015· article· en· W2025641593 on OpenAlexafffund
Dyhia Belhabib, U. Rashid Sumaila, Vicky W. Y. Lam, Dirk Zeller, Philippe Le Billon, Elimane Abou Kane, Daniel Pauly

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersPaul G. Allen Family FoundationUniversity of British ColumbiaMAVA FoundationPew Charitable Trusts
KeywordsFishingChinaEuropean unionEurosGeographyFisheryEquity (law)Market accessValue (mathematics)BusinessAgricultural economicsInternational tradeEconomicsPolitical scienceBiology

Abstract

fetched live from OpenAlex

We compare the performance of European Union (EU) and Chinese fisheries access agreements with West African countries in terms of illegal and unreported fishing, economic equity, and patterns of exploitation. Bottom-up re-estimations of catch reveal that the EU (1.6 million t•year(-1)) and China (2.3 million t•year(-1)) report only 29% and 8%, respectively, of their estimated total catches (including estimated discards whenever possible) from West African countries between 2000 and 2010. EU catches are declining, while Chinese catches are increasing and are yet to reach the historic maximum level of EU catches (3 million t•year(-1) on average in the 1970s-1980s). The monetary value of EU fishing agreements, correlated in theory with reported catches, is straightforward to access, in contrast to Chinese agreements. However, once quantified, the value of Chinese agreements is readily traceable within the African economy through the different projects they directly cover, in contrast to the funds disbursed [to host governments] by the EU. Overall, China provides resources equivalent to about 4% of the ex-vessel value [value at landing] of the catch taken by Chinese distant-water fleets from West African waters, while the EU pays 8%. We address the difficulties of separating fees directly related to fishing from other economic or political motivations for Chinese fees, which could introduce a bias to the present findings as this operation is not performed for EU access fees officially related to fishing. Our study reveals that the EU and China perform similarly in terms of illegal fishing, patterns of exploitation and sustainability of resource use, while under-reporting by the EU increases and that by China decreases. The EU agreements provide, in theory, room for improving scientific research, monitoring and surveillance, suggesting a better performance than for Chinese agreements, but the end-use of the EU funds are more difficult, and sometime impossible to ascertain.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.125
GPT teacher head0.271
Teacher spread0.146 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations116
Published2015
Admission routes2
Has abstractyes

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