Survival of the Richest, not the Fittest: How attempts to improve governance impact African small-scale marine fisheries
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.
Bibliographic record
Abstract
The sustainable use of fisheries resources is a priority of the African Union in developing the Blue Economy (BE). Growing global demand for seafood has attracted diverse actors to African waters, including Distant Water Fishing Nations (DWFNs) fleets. Complex fisheries governance challenges, unsustainable rates of fishing and rising fisheries-related crimes have ensued. To reverse these impacts, some African states are deploying various fisheries governance mechanisms. Drawing on extensive expert experiences, the review of literature, fisheries databases, international and regional agency reports, NGO and government reports and case studies from West and East Africa, we demonstrate two critical findings. First, fisheries governance mechanisms in Africa act largely to constrain small-scale fisheries (SSF) while failing to contain the industrial fisheries sector, resulting in the marginalisation of the SSF. Secondly, despite a higher incidence of Illegal, Unreported and Unregulated (IUU) fishing in industrial fisheries than the SSF, fisheries governance mechanisms continue to advance the 'Survival of the Richest' – the industrial sector, to the detriment of the 'Fittest' – the SSF. The SSF supports millions of jobs and is better adapted to meet the continents' nutrition and socio-economic security. For the fisheries sector to contribute to the sustainable development of Africans, states must redirect governance towards regulating the industrial sector, emphasising equitable access for the SSF whilst prioritising ecological sustainability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it