Contributions by Women to Fisheries Economies: Insights from Five Maritime Countries
Bibliographic record
Abstract
The contribution by women to fisheries economies globally continues to be overlooked, in part, because “fishing” is often narrowly defined as catching fish at sea, from a vessel, using specialized gears. Both men and women are involved in fisheries, but often in different roles and activities. Fisheries research, management, and policy have traditionally focused on direct, formal, and paid fishing activities—that are often dominated by men, ignoring those that are indirect, informal, and/or unpaid—where women are concentrated. This has led to a situation where men's and women's contributions to fisheries are not equally valued or even recognized and has resulted in women being largely excluded from fisheries decision-making processes. Here, we examine the contributions by women in the fisheries sector of five globally significant marine fishing countries—Mexico, Peru, Senegal, South Africa, and Vietnam. These countries each have strong links between livelihoods and marine capture fisheries, yet represent different geographic, socioeconomic, and governance contexts. Through a synthesis of existing data, case studies, and consultation with local experts, we found that the contribution by women to the fisheries of these five countries is substantial. However, this investigation also revealed major gaps in understanding of gender inequalities in the fisheries sector and the need for better gender-disaggregated data to inform fisheries policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".