Illuminating the multidimensional contributions of small-scale fisheries
Bibliographic record
Abstract
Sustainable development aspires to “leave no one behind”1. Even so, limited attention has been paid to small-scale fisheries (SSF) and their importance in eradicating poverty, hunger and malnutrition. Through a collaborative and multidimensional data-driven approach, we have estimated that SSF provide at least 40% (37.3 million tonnes) of global fisheries catches and 2.3 billion people with, on average, 20% of their dietary intake across six key micronutrients essential for human health. Globally, the livelihood of 1 in every 12 people, nearly half of them women, depends at least partly on small-scale fishing, in total generating 44% (US$77.2 billion) of the economic value of all fisheries landed. Regionally, Asian SSF provide fish, support livelihoods and supply nutrition to the largest number of people. Relative to the total capture of the fisheries sector (comprising large-scale and small-scale fisheries), across all regions, African SSF supply the most catch and nutrition, and SSF in Oceania improve the most livelihoods. Maintaining and increasing these multidimensional SSF contributions to sustainable development requires targeted and effective actions, especially increasing the engagement of fisherfolk in shared management and governance. Without management and governance focused on the multidimensional contributions of SSF, the marginalization of millions of fishers and fishworkers will worsen. A study aimed at revealing the role of small-scale fisheries in sustainable development shows they provide at least 40% of the global fishing catch and affect the livelihoods of 1 in 12 people in the world, among other important contributions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".