Poverty line income and fisheries subsidies in developing country fishing communities
Bibliographic record
Abstract
<title>Abstract</title> 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 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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".