Program Berbasis Masyarakat dalam Upaya Pengurangan Risiko Bencana di Kabupaten Pandeglang
Bibliographic record
Abstract
Since 1950, the need for global economy related to natural disasters has increased 14 fold. The natural disaster that occured in 2018, put Indonesia into the country with the highest number of victims in the world, which was caused by three rare phenomena. This study discusses about the Desa Tangguh Bencana program as a strategic step for community-based disaster risk reduction in the Pandeglang Regency, which is one of the areas that has the impact of damage and casualties from the tsunami phenomenon in the Sunda Strait. This study aims to obtain information about the activity steps and communication strategies of the program which refers to the concept of disaster mitigation according to George D. Haddow and Kim S. Haddow in 2014. The research method uses a descriptive qualitative approach with an interpretive paradigm. Data was collected through semi-structured interviews and other supporting documents, which were analyzed using the Miles & Huberman method and tested for credibility by triangulation of sources. The results showed that community participation in this program is the main key as a planning initiator to the implementing activities. These disaster risk reduction efforts are carried out by identifying community risks, determining action plans, funding, involving opinion leaders as parties raised by the community, forming messages, to the communication channel mechanism.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".