Urban water insecurity and its gendered impacts: on the gaps in climate change adaptation and Sustainable Development Goals
Bibliographic record
Abstract
It is commonly accepted that water insecurity, accelerated by climate change, is experienced by women in gender specific ways. Using a rapid review methodology this paper evaluates existing literature (2014–2021) on climate change adaptation in relation to water (SDG6) and gender (SDG5) in urban and peri-urban contexts. By analyzing water, gender, and adaptation literature a thematic mapping of SDG5 was done on the resulting 34 documents. Despite methodological limitations – time constraints, exclusion of gender-sustainable development literature, and narrow inclusion criteria – this paper finds a paucity of research in this space during the time period under study. Most literature focuses on low- and middle-income countries, primarily Asia and sub-Saharan Africa, to the exclusion of South America. Notably, evidence demonstrating interlinkages between SDG5 and climate change adaptations in the WaSH sector and gender sensitive dissemination of disaster warnings is lacking. Adaptation strategies resulting in negative impacts on women undermine SDG5 and maladaptive behaviours related to management of domestic water supply and disaster-risks are particularly concerning in this context. Subsequently, this paper establishes the need for practical research assessing the gendered dimensions of all adaptations, including research demonstrating interlinkages between adaptations, women-specific benefits, and strengthened legislation to promote gender equality and empowerment.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".