Points de vue féministes et communautaires sur l’adaptation aux catastrophes: Récits de femmes de communautés locales des zones humides du Bangladesh
Bibliographic record
Abstract
This paper offers an in-depth exploration of the critical role played by women within vulnerable wetland communities in Bangladesh, particularly in the context of disaster adaptations. As climate change-induced disasters become increasingly prevalent, it is essential to recognize women’s agency, knowledge, and resilience within these communities, and between minority Hindu and majority Muslim women. Employing a feminist framework, this research delves into the nuanced dynamics of gender, faith, and community-based disaster adaptation strategies. Through narratives and stories from local women, the paper unveils the innovative and adaptive approaches often overlooked in conventional disaster management practices, the heightened agency of majority Muslim women and their “witnessing” of the suffering of the minority Hindu women. It highlights the intersectionality of gender, faith, poverty, and environmental vulnerability, shedding light on the unique challenges faced by women in wetland areas, especially vulnerable Indigenous and Hindu minority women. The findings of this paper underscore the need for more inclusive, gender-responsive disaster policies and programs, and call for a shift away from top-down approaches to more participatory, community-led solutions. By amplifying the voices and experiences of local women in Bangladesh, this paper contributes to a broader discourse on sustainable disaster adaptation strategies, ultimately striving for greater equity and resilience in the face of climate-related challenges.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".