Women’s experiences in influencing and shaping small‐scale fisheries governance
Bibliographic record
Abstract
Abstract This paper synthesizes current empirical evidence on how women experience, shape and influence small‐scale fisheries (SSF) governance. Our synthesis addresses an important gap in the literature, and helps highlight the opportunities to improve women's participation in governance and advance gender equality. We identified, characterized and synthesized 54 empirical cases at the intersection of gender and SSF governance, which comprise the relevant body of literature. Our review confirms the need to embed gender in the empirical examination of SSF governance towards expanding the current evidence base on this topic. We found that the institutional contexts within which women participate reflect a broad spectrum of arrangements, including the interactions with rules and regulations; participatory arrangements such as co‐management; and informal norms, customary practices and relational spaces. We also synthesized a typology of governance tasks performed by women in SSF. The typology includes leadership roles and active participation in decision‐making; relational networking and collective action; exercising agency and legitimacy; resource monitoring; knowledge sharing; meeting attendance (with no/less participation in decision‐making); and activism and mass mobilization. Furthermore, we drew broader insights based on the patterns that emerged across the literature and highlighted implications for improving women's meaningful participation in SSF governance. For example, exploring the breadth of governance arrangements to include all governance spaces where women are active, adjusting governance arrangements to respond to current and emerging barriers, and recognizing how women's efforts link with societal values may help legitimize their representation in SSF governance. Findings of this review should be of interest to the scholarly community, practitioners and policymakers alike and inform future research agendas, policy dialogues and practice intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".