Casting the Net Widely: Effective Governance and the Contribution of Fisheries to the Development of African Countries
Bibliographic record
Abstract
Africa’s marine fisheries and oceans have contributed significantly to the livelihood of the continent’s coastal communities for centuries. The shell middens found off the coast of Eritrea in the Red Sea are the oldest record of human consumption of sea food (Walter et al., 2000; Mayer and Beyin, 2009). The Fantis of Ghana have been fishing along the West African coast since the 18th century (Alder and Sumaiia, 2004; Atta-Mills et al., 2004). Marine resources could continue to serve as a sustainable source of economic development. That is, social and cultural values for coastal African countries if marine resources are managed and governed effectively with regard to the environment. In this chapter, we explore the opportunities and challenges facing African fisheries, with the objective of providing insights for policy-makers and the public, to help them develop policies for the sustainable development of African fisheries, both for current and future generations. By sustainability we here mean the ability to maintain the regeneration potential of fisheries resources indefinitely into the future so that they can support the social and economic needs of the society for many generations to come. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".