Are fishers poor? Getting to the bottom of marine fisheries income statistics
Bibliographic record
Abstract
Abstract Fishers’ economic status is hard to assess because fisheries socio‐economic data, including earnings, are often not centrally available, standardized or accessible in a form that allows scaled‐up or comparative analyses. The lack of fishing income data impedes sound management and allows biased perceptions about fishers’ status to persist. We compile data from intergovernmental and regional data sets, as well as case‐studies, on income earned from marine wild‐capture fisheries. We explore the level and distribution of fishers’ income across fisheries sectors and geographical regions, and highlight challenges in data collection and reporting. We find that fishers generally are not the poorest of the poor based on average fishing income from 89 countries, but income levels vary widely. Fishing income in the large‐scale sector is higher than the small‐scale sector by about 2.2 times, and in high‐income versus low‐income countries by almost 9 times. Boat owners and captains earned more than double that of crew and owner‐operators, while income from fisheries is greater than that from agricultural work in 63% of countries in this study. Nonetheless, incomes are below national poverty lines in 34% of the countries with data. More detailed fishing income statistics is needed for quantitative scientific research and for supporting socio‐economic policies. Key gaps to address include the lack of a centralized database for fisheries income statistics and the coarse resolution at which economic statistics are reported internationally. A first step to close the gap is to integrate socio‐economic monitoring and reporting in fisheries management.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".