Gender and small‐scale fisheries: a case for counting women and beyond
Bibliographic record
Abstract
Abstract Marine ecosystem–scale fisheries research and management must include the fishing effort of women and men. Even with growing recognition that women do fish, there remains an imperative to engage in more meaningful and relevant gender analysis to improve socio‐ecological approaches to fisheries research and management. The implications of a gender approach to fisheries have been explored in social approaches to fisheries, but the relevance of gender analysis for ecological understandings has yet to be fully elaborated. To examine the importance of gender to the understanding of marine ecology, we identified 106 case studies of small‐scale fisheries from the last 20 years that detail the participation of women in fishing (data on women fishers being the most common limiting factor to gender analysis). We found that beyond gender difference in fishing practices throughout the world, the literature reveals a quantitative data gap in the characterization of gender in small‐scale fisheries. The descriptive details of women's often distinct fishing practices nonetheless provide important ecological information with implications for understanding the human role in marine ecosystems. Finally, we examined why the data gap on women's fishing practices has persisted, detailing several ways in which commonly used research methods may perpetuate biased sampling that overlooks women's fishing. This review sheds light on a new aspect of the application of gender research to fisheries research, with an emphasis on ecological understanding within a broader context of interdisciplinary approaches.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".