Poverty line income and fisheries subsidies in developing country fishing communities
Bibliographic record
Abstract
Abstract Eradicating poverty and harmful fisheries subsidies are two pressing challenges frequently addressed in international agendas for sustainable development. Here we investigate a potential solution for addressing both challenges simultaneously by asking the hypothetical question: to what extent can harmful fisheries subsidies provided by a country finance the cost of lifting fishers out of poverty? Focusing on 30 coastal least developed countries, we find that fishers in 87% of these countries do not earn sufficient income to satisfy the extreme poverty line income of USD 1.90/person/day, and that it costs an estimated USD 2.2 to 2.6 billion to lift these fishers to different levels of poverty line incomes. Our analysis further suggests that at the country level, redirected harmful fisheries subsidies can cover the entire cost of covering the poverty income gap for between 37 to 43% of assessed countries. Our results provide quantitative evidence that can be used to support simultaneous progress towards achieving several Sustainable Development Goals, including those dealing with poverty reduction, food insecurity, and ocean sustainability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".