Efundja as a risk driver and change agent for the Cuvelai-Etosha basin rural communities
Bibliographic record
Abstract
Floods are one of the persistent major risk drivers impacting the Cuvelai-Etosha basin of northern Namibia. Locally known as Efundja, this disruptive event negatively impacts particularly the rural population, who have limited resources to combat its effects. Being mostly subsistence farmers in isolated communities, the floods wreak havoc with their homesteads, harvests, animals, and general way of life by cutting them off from their fields, neighbours, and essential services for prolonged periods. This study investigates the impacts and coping mechanisms of rural communities regularly affected by Efundja. Data was collected from four groups of respondents through interviews and focus groups. These were heads of households in the affected rural communities, the community leaders, local councillors and national government officials involved in disaster mitigation. This ensured a comprehensive picture of the impacts. Contribution: Despite the presence of a national disaster risk management strategy, the national disaster response mechanism rather reactively responds to the hazard as opposed to being proactive. Results indicates that the strategy is not fully implemented and the parts that are implemented functions as a top-down approach. Respondents reported a wide range of impacts and a general inability to effectively cope with Efundja, coupled with an absence of their voices in deliberations about risk reduction matters. Additions to the current disaster risk management strategy is proposed and several recommendations derived from the research results concludes the article. Should these recommendations be implemented into the Namibian disaster risk management strategy, Efundja as risk driver will also become an agent of change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".