Harmonizing Disaster Risk Reduction and Climate Change Adaptation Frameworks for Risk Informed Development Planning in Sub-Saharan Africa: The Case of Uganda and Malawi
Bibliographic record
Abstract
Increasing impacts of disasters and climate hazards have prompted Africa countries to develop national disaster risk reduction (DRR) strategies with the aim of reducing mortality and other losses. However, disasters still have a significant impact on their populations, their livelihoods, and the infrastructure on which they depend. Furthermore, an increasing understanding of the need to address the root causes of risk has led to calls for greater coherence between strategies that focus on DRR, Climate Change Adaptation and Sustainable Development. There is acknowledgement of the existing implementation gap dividing the policy domains of Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) at the national as well as international levels. This paper analyses the gaps and opportunities in design and implementation of policies and practices within the two domains and suggest measures to enhance their collaboration in Malawi and Uganda. Document analysis and interviews with 8 respondents over a period of one month were undertaken to gather the needed information. Fostering conceptual understanding of resilience, joint planning and implementation of similar activities through a common coordination mechanism were found to be essential for achieving coherence across the five thematic areas.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".