How Far Disaster Management Implemented Toward Flood Preparedness: A Lesson Learn from Youth Participation Assessment in Indonesia
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".