Valuing invisible catches: Estimating the global contribution by women to small-scale marine capture fisheries production
Bibliographic record
Abstract
The role that women play in fisheries around the world is receiving increasing international attention yet the contributions by women to fisheries catches continues to be overlooked by society, industry and policy makers. Here, we address this lack of visibility with a global estimation of small-scale fisheries catches by women. Our estimates reveal that women participate in small-scale fishing activities in all regions of the world, with approximately 2.1 million (± 86,000) women accounting for roughly 11% (± 4%) of participants in small-scale fishing activities, i.e., catching roughly 2.9 million (± 835,000) tonnes per year of marine fish and invertebrates. The landed value of the catch by women is estimated at USD 5.6 billion (± 1.5 billion), with an economic impact of USD 14.8 billion per year (± 4 billion), which is equivalent to 25.6 billion real 2010 dollars (± 7.2 billion). These catches are mostly taken along the shoreline, on foot, or from small, non-motorized vessels using low-technology, low-emission gears in coastal waters. Catches taken by women are often for home consumption, and thus considered part of the subsistence sub-sector. However, in many contexts, women also sell a portion of their catch, generating income for themselves and their families. These findings underscore the significant role of women as direct producers in small-scale fisheries value chains, making visible contributions by women to food and livelihood security, globally.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".