How Far Disaster Management Implemented Toward Flood Preparedness: A Lesson Learn from Youth Participation Assessment 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
Flood is a common and frequent natural disaster in many countries that causes huge economic losses and casualties every year. Youth participation in flood disaster management (FDM) has not been much explored, especially in the non-prone area but contributing to flooding resilience. Therefore, this study aims to identify youth participation in disaster management to help an improvement in preparedness action. The research was conducted using a qualitative model: case study research, involving 191 young people aged 14-35-years in 16 sub-districts in Semarang City. The data, including youth’s action, knowledge, and participation in FDM, was collected using Google Form, observation, and interview, then statistically analyzed using Mann-Whitney’s test and path analysis. The results show the respondents in flood-affected areas are more actively participating in flood disaster management action because of their experience in facing flooding. Also, the planning step is significantly influenced by the FDM implementation. The planning process is the main defining factor in disaster management successfulness and essentially affecting mitigation, rehabilitation, and evaluation steps. The level of youth participation is deemed necessary to be increased to develop a more comprehensive disaster management program according to regional needs. We suggest that FDM should be transformed into disaster awareness which is delivered through education, socialization, training, and/or flood disaster response simulations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it