Implementation of Military Incident Management System in Disaster Management in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indonesia’s success in disaster management cannot be separated from the military’s role. The military plays a strategic role by mobilizing military resources on a massive scale through the military command system. However, the ability of Indonesian Army (TNI AD) soldiers and organizations, in general, is considered to have limited capabilities specifically for personnel handling natural disasters. This research aims to map the disaster management implemented by the Indonesian Army in disaster response through the Incident Management System. Data collection was conducted interactively through qualitative methods with in-depth interviews with the Indonesian Army’s Supply and Transportation Unit (Pusbekangad). The research results show that the Indonesian Army (TNI AD) has competent resources in disaster response, involving the Indonesian Army’s Supply and Transportation Unit, which has primary skills and capabilities in logistics and transportation. These capabilities are facilitated by the Incident Management System, which is structured, systematic, and well-organized. The Incident Management System built by the Indonesian Army involves an incident commander, operation section, planning section, logistics section, finance/administration section, driver section, and the cooking team as a trained, capable, experienced, and ready-to-deploy ad-hoc organization in all operational areas. Indonesian Army uses the Incident Management System to respond to disasters such as earthquakes in Cianjur, South Kalimantan floods, and West Sulawesi floods. The Incident Management System serves
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it