Climate Disasters, Mass Violence, and Human Mobility in South Sudan: Through a Gender Lens
Bibliographic record
Abstract
This article examines the links between gender, mass violence, climate change, and displacement in South Sudan. I argue for risk-informed gender-sensitive strategies that incorporate local capacities and sources of resilience. When civil war engulfed South Sudan again in 2013, egregious human rights violations, including sexual and gender-based violence, were perpetrated with near complete impunity. As the national army was divided along Dinka-Nuer ethnic lines, soldiers from each faction turned against each other in a deadly pattern of revenge and counter-revenge attacks that soon spread across the national territory. Inter-communal conflicts also intensified, often centering on competition over land for pasture, cattle raiding, and the abduction of women and children. Additionally, environmental challenges, including both droughts and severe flooding, as well as locust swarms, have resulted in widespread crop loss and property damage. Famine was declared in 2017, with current conditions classified as widespread acute food insecurity and acute malnutrition. The intersection of these multiple crises has displaced nearly 4 million people. Despite these seemingly insurmountable challenges, South Sudanese women have made significant strides in their push for inclusion in national peace processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| 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".