Stories from the Front Line: Coping Strategies for Flood Disasters among the Dinka Community of Bor County, South Sudan
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Bibliographic record
Abstract
The frequency of floods in Jonglei State, Bor County of South Sudan has significantly increased in recent times due to climate change.Bor County has the highest frequency of floods, making this area highly vulnerable.We conducted a qualitative study in five Payams of Makuach, Anyidi, Baidit, Kolnyang and Jalle.A total of ten Focus Group Discussions (FGDs) and twenty Key Informant Interviews (KIIs) were conducted.We used trained research assistants (moderator and note taker) to collect data.All research tools were initially translated to local Dinka language.All discussions were audio taped, and were transcribed verbatim before analysis.We explored both coping strategies and underlying causes of vulnerability.Data were analysed using latent content analysis through identifying codes from which basis categories were generated and grouped into themes.Results of the study show that the positive coping strategies used to deal with floods in Bor County included: adoption of good farming methods, support from government and other partners, livelihood diversification and using indigenous knowledge in weather forecasting and preparedness.Relocation was identified as unsustainable because people often returned back to high-risk areas due lack of public participation in decision making.The main causes of vulnerability were: poverty, lack of formal education, people inhabiting high risk areas, lack of formal education and knowledge on flood preparedness and, cultural beliefs affecting people's ability to cope.This study has revealed that deep rooted links to poverty, lack of formal education and low levels of knowledge on flood preparedness were responsible for failure to overcome the effects of floods in vulnerable areas of Bor County.However, support from the government and implementation partners was identified to be effective in enabling the community to reduce the negative effects of floods.This calls for high impact innovative interventions focused on addressing these underlying causes as well as public participation of all stakeholders in scaling How to cite this paper:
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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