A landscape study on the intersection between climate change and gender-based violence in Uganda
Bibliographic record
Abstract
Despite global evidence linking the degradation of the environment caused by humans and gender-based violence (GBV), current climate change mitigation and adaptation strategies and GBV programming remain unconnected. We sought to address this gap by identifying organizations with climate change adaptation and/or mitigation and GBV prevention activities based in Kampala, Uganda, and describing their existing programming, challenges, and vision to develop better preparedness and response mechanisms to address the climate change related increase in the risk of GBV. We first aimed to identify non-governmental organizations (NGOs) in Uganda with GBV and/or climate change programming through an internet-based search. In a second phase, staff from identified NGOs were invited to participate in an interview, aiming to obtain a broad picture of current GBV and climate change programs, understand programing challenges and gaps encountered in the field, and learn about their view on the interlinkages between GBV and climate change. Of 55 NGO’s identified in the internet search having GBV (29) or climate change programing (26), 29 were available for interviews (15 from GBV and 13 from climate change). Inductive themes emerging from interviews fell into four main areas related to 1) discriminatory norms and practices that drive GBV, 2) how climate change issues fuel GBV, 3) how COVID-19 amplified existing GBV issues, and 4) gaps and challenges in current GBV and climate change mitigation and adaptation programing in Uganda. Our findings support the interconnection between GBV and climate change in Uganda by highlighting the intersectional vulnerabilities and impacts experienced by women and girls. Overall, results show that climate change exacerbates prevailing gender inequalities and can increase the risk of GBV in several ways. Implications for future programing and policy are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".