Disaster Management and Emergency Response Capability Assessment Indexes in Tanzania; Empirical Evidence from Dar es Salaam City
Bibliographic record
Abstract
The disaster management and emergency response capability assessment are put forth in this study to offer broad guidelines for various emergency management organization types. To evaluate the organization’s capability, five (policy and Legislations, infrastructure, personnel, technology, inter-agency coordination) elements are proposed from the capability assessment results, and suggestions are made for how the elements’ specifics should be. An improved mutual understanding of each agency’s perspective, resources, and capabilities for disaster management and emergency response operations turned out to be a significant advantage for this study. The study applied focus groups, expert consultation, and content analysis to various relevant documents to establish capabilities assessment indexes relevant to disaster management and emergency response capabilities in Tanzania. The application of Delphi method for expert consultations, the weights of indexes were determined using analytic hierarchy process and proportional distribution method. The existing emergency response capabilities were then evaluated and proved to be significant to improve the capabilities to both disaster management and emergency response in the city and Tanzania. To improve the capabilities for disaster management and emergency response operation in Tanzania, the study recommends to harmonize disaster management and emergency response regulations, policies and frameworks to align disaster management and emergency response strategies with international standards and the actual situation in the ground. Also to grab the investment opportunities in disaster management and emergency response infrastructure, technology, deployment of Artificial Intelligence, enhancement of collaborative governance and deep trust building among stakeholders and personnel development by prioritizing the workable systems (early warning, fire protections, public awareness campaigns) and trainings to personnel directly involved in the ground and planning levels.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".