Improvement of the business model of the disaster management system based on the service design methodology
Bibliographic record
Abstract
The type and scale of disasters are changing with the changing social structures in modern society. Natural, social and human disasters occurred individually in the past, but the complexity and scale of these disasters have increased recently. As a result, National Disaster Management Institute (NDMI) has been operating the Smart Big Board (SBB) system to ensure effective real-time disaster management since June 2013. Based on Web GIS, this system can rapidly manage various types of information pertaining to disasters. However, it has not been able to satisfy all users because it was not developed keeping in mind the needs of service users. This study attempts to improve the SBB service using the service design methodology that is currently being widely used to improve public services. The service design process is conducted in accordance with the double diamond model, which utilizes a customer journey map to locate the contact point between user and service. This improved system is especially able to perform user customized disaster management in response to various and complex disaster types. If the improved system is applied to the national emergency management system through the business model design process, it will be able to effectively manage any future disasters.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".