Urban water insecurity and its gendered impacts: on the gaps in climate change adaptation and Sustainable Development Goals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it