Sustainable fisheries are essential but not enough to ensure well‐being for the world’s fishers
Bibliographic record
Abstract
Abstract Effective fisheries management is necessary for the long‐term sustainability of fisheries and the economic benefits that they provide, but focusing only on ecological sustainability risks disregarding ultimate goals related to well‐being that must be achieved through broader social policy. An analysis of global landings data shows that average fishing wages in 36%–67% of countries, home to 69%–95% of fishers worldwide, are likely below their nationally determined minimum living wage (which accounts for costs of food, shelter, clothing, health and education). Furthermore, even if all fisheries in every country were perfectly managed to achieve their Maximum Sustainable Yield, a common sustainability target, average incomes of fishers in up to 49 countries—70% of fishers worldwide—would still not meet minimum living wages. Access to decent work and livelihoods are fundamental human rights, including for all fisherfolk around the world, and strategies to support their well‐being must therefore integrate a much wider set of perspectives, disciplines and institutions. Key first steps for fisheries researchers are to more fully recognize and estimate fisheries benefits to households—including income from women and/or alternative employment, unreported landings, or shadow values of subsistence catch—and to help identify and learn from economic equity outcomes in rebuilt fisheries around the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".