Climate-resilient aquatic food systems require transformative change to address gender and intersectional inequalities
Bibliographic record
Abstract
The adverse impacts of climate change on aquatic food systems (AFS) and the people who depend on AFS for livelihood security are inequitably distributed between and within countries. People facing the highest risks and experiencing the severest impacts of climate change are those who already experience multidimensional inequalities in their lives, particularly because of their gender, class, age, indigeneity, ethnicity, caste, religion, and the physical and political conditions that can create additional vulnerabilities. In this paper, we conducted a scoping review of the literature that explores the links between climate change, gender, and other social identities, and AFS. The review was complemented by an analysis of representative data on women and men aquaculture farmers in Bangladesh from 2018 to 2019. We also analysed data from the 2019 Illuminating Hidden Harvest project. The study relied on the gendered agrifood system and aquatic food climate risk frameworks to guide on literature search, review, and data analyses. Our findings show that intersecting identities disadvantage certain AFS actors, particularly young women from minority ethnic groups, and create challenge for them to manage and adapt to climate shocks and stresses. Examples of gender-responsive and transformative interventions are highlighted from our review to showcase how such intersectional disadvantages can be addressed to increase women’s empowerment and social and gender equality.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".