Evaluating the Implementation of BNPB’s Srikandi Bencana Program in Dharma Wanita Persatuan UNNES
Bibliographic record
Abstract
One of the vulnerable-to-disasters parties is women. In fact, women have great potential to take part in creating a disaster-resilient society. Dharma Wanita Persatuan (Women’s Association) as an organization consisting of the wives of civil servants or female civil servants in government agencies has so far only played an informal role as a supporter of their husbands and has not been empowered. If members of Dharma Wanita are empowered through the Srikandi Bencana (Disaster Heroine) program, they have the potential to become a driving actor in increasing preparedness in the community where they live. This quantitative study aims to analyze the level of knowledge of members of the Dharma Wanita Persatuan UNNES about the Srikandi Bencana program of National Agency for Disaster Countermeasure (BNPB) and analyze the level of preparedness of members of the Dharma Wanita Persatuan UNNES. This study involved 50 members of the Dharma Wanita Persatuan UNNES. Data was collected by using a questionnaire method using google forms. The data analysis techniques used in this study include quantitative descriptive analysis techniques. The results point out that the average knowledge of members of the Dharma Wanita UNNES regarding the Srikandi Bencana is still relatively low at 33.33%. Then, the average level of their preparedness is in the medium category, namely at 68.13%. These numbers indicate that the Srikandi Bencana program must be campaigned more massively and realized among the Dharma Wanita. If the members are equipped with adequate disaster preparedness, they will play as notable actors to establish alertness in their families and society. This way, disaster risk reduction will also be more gender-friendly that everyone can participate.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".