Making the case for gender-inclusive fisheries governance, policies and climate adaptation
Bibliographic record
Abstract
Gender equality has been a key consideration for policymakers and natural resource managers in assessing climate risk and developing effective adaptation strategies. However, the interests and concerns of women in relation to climate-related planning and fisheries policies are often neglected. This underrepresentation of women, particularly from developing countries, poses a risk of overlooking opportunities to support vulnerable fishing communities. Additionally, it inadvertently increases the vulnerability of marginalized women fisherfolk. This paper reviews 122 refereed publications on the empowerment of local fishing communities, gender participation in fisheries governance, development, and the need to consider gender dimensions in climate adaptation programs worldwide. It highlights the socio-economic impacts of climate change on livelihood and discusses potential adaptation measures. The findings support the adoption of frameworks and policies that provide alternative metrics for women's empowerment, inclusion in fisheries governance, and climate adaptation strategies. The study also offers recommendations for governments, non-governmental organizations, and development agencies responsible for fisheries governance and climate adaptation initiatives.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".