Euros vs. Yuan: Comparing European and Chinese Fishing Access in West Africa
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".