Four-Stage Framework for Implementing a Chatbot System in Disaster Emergency Operation Data Management: A Flood Disaster Management Case Study
Bibliographic record
Abstract
This research proposes a four-stage consultant framework for applying a chatbot as a data management system. With the advancement of computational power and data storage technology, the increasing amount of data makes the issue of data management difficult to address. Management of a massive amount of data by utilizing chatbots to play the roles of a data manager and a data provider has been extensively studied. Although a chatbot system has been proven to increase the overall efficiency of data management, implementing a chatbot system in a government department remains a challenge, especially in a field with highly complex data. This research presents the authors’ experience of applying a chatbot system in a department of the government of Taiwan for disaster response operations. A four-stage consulting framework comprising 1) existing workflow review, 2) usability evaluation, 3) system improvement, and 4) management plan (EUSM) was thus proposed. After a two-year field test, the authors found that the framework could help the department in clarifying their working process, increase the overall efficiency of the chatbot system, and identify the major issues of introducing the chatbot system.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".