FLOOD DISASTER PREPAREDNESS STUDY IN BANJAR CITY, WEST JAVA
Bibliographic record
Abstract
Flooding is a very common natural disaster in Indonesia. It is necessary to have a disaster risk management programme. This study aims to determine the disaster preparedness of the community for flood in the city of Banjar, West Java. Data were collected from the city government, regional disaster management agency, non-governmental organisations, BPS, earth shape maps from InaGeoportal, vulnerability map data, vulnerability maps and risk maps from the National Disaster Management Agency, and literature in the form of journals and books. The data collection techniques used by the researchers included questionnaires and documentation. The method of data analysis used was descriptive percentage and scoring analysis to analyse the frequency distribution of the level of preparedness of the community in the face of flood disasters.The data was obtained by providing questionnaires to the community which were filled in by the respondents and then calculating the total frequency of correct answers from each respondent. It can be seen that Banjar city has a high level of flood hazard, vulnerability and capacity, which means that it is important to take preparedness measures to reduce the adverse effects and anticipate the increase of recurrent floods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".