Drought Preparedness and Response as if Development Matters: Case Studies from Kenya
Bibliographic record
Abstract
Few now question the link between disasters and development. The notion that vulnerability is the root cause of disasters and that it accrues from social processes and human decisions, is no longer contentious. However this convergence has not translated into mainstream practice of either development planning or emergency response communities. Vulnerability analysis and disaster risk reduction remain at the margins. Projects that bridge relief and development do not readily attract donor funding. Some exceptions have been documented. There are development NGOs involved in disaster response, and humanitarian assistance NGOs that have integrated vulnerability reduction in their disaster relief work. This paper adds to this body of literature. Based on field research in two drought prone communities in Kenya it assesses the effectiveness of the efforts of government and NGOs in integrating drought management and long-term development in their community interventions and their impacts on community vulnerability. Key informant interviews complemented review of documents and site visits. The paper concludes that while most initiatives, such as water conservation, livestock rearing, income diversification are successful in reducing short term vulnerability and have the potential for contributing to long-term community resilience, others appear to be creating dependency. They warrant careful study and systematic community involvement in order to develop appropriate and sustainable strategies. Keywords: vulnerability reduction, disaster risk reduction, food relief, disaster preparedness and response
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".