Unfolding the Contributions of Small-Scale Fisheries to the Sustainable Development Goals
Bibliographic record
Abstract
The effects of climate change are hindering the ability of the world to achieve the Sustainable Development Goals (SDG) by 2030. In particular, the success of SDG 2 (Zero hunger) is threatened by the impacts of climate change on global food production, leaving over 20% of the world's population at risk of food and nutrition insecurity. Fisheries, particularly small-scale fisheries (SSF), play a crucial role in future global food security. With the constant increase in demand for aquatic food products and its key role in nutrition in many coastal contexts, sustainable fishery production is essential to ensure healthy food while protecting the health and function of marine ecosystems. Despite its importance for livelihoods and nutrition for millions of people, SSFs remain poorly acknowledged in global policies. Social-ecological relationships in SSF are complex and poorly understood, making it difficult to formulate policies that could improve and preserve the contributions of SSF to sustainable development. Here, we developed an expert-based rapid appraisal framework to identify and characterize the contribution of SSF to SDGs. We implemented a flexible scoring system for data-limited situations, usable with natural resources users, managers, and scientists. Our structured approach is not limited to SDG 14 and target 14.b; rather, it provides insights into SSF's contributions to 11 other SDGs. This research discusses the findings from the application of the Rapid Appraisal framework to 60 SSF case studies in eight countries across Africa, Europe, and Latin America. Our findings indicate that SSF have consistent potential to advance certain SDGs and targets, especially targets 1.4, 12.3, 1.1, 8.5, and to a lesser extent targets 14.2, 14.1, and 16.7. SSFs impact on other targets are variable and dependent on local contexts, especially some targets of SDGs 5 (targets 5.5 and 5.A) and 8 (targets 8.7, 8.8, and 8.9). Our work reveals that unlocking SSFs potential to advance SDGs, requires understanding them not only from the marine resource perspective (SDG 14) but also from its social and economic components. Our study provides the first comprehensive approach for assessing the multiple contributions of SSFs to SDGs, allowing for a global assessment of SSF across diverse contexts, and analyzing key trends and variations in their contributions to the SDGs. As SSFs supply about 40% of the global fish catch and 90% of the employment in the capture fisheries sector, we argue that SSFs play a critical role in policies leading towards the SDGs.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".