Contributions by Women to Fisheries Economies: Insights from Five Maritime Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it