Pengaruh Modal Sosial Terhadap Kesiapsiagaan Masyarakat Dalam Menghadapi Bencana Banjir
Bibliographic record
Abstract
Indonesia adalah negara rawan bencana, salah satunya yaitu bencana banjir yangdisebabkan oleh kondisi geografis. Diperlukan kesiapsiagaan masyarakat dalam mengatasibencana tersebut. Modal sosial menjadi salah satu faktor penting dalam manajemenbencana terutama dalam kesiapsiagaan. Tujuan penelitian ini untuk mengetahui pengaruhmodal sosial terhadap kesiapsiagaan masyarakat dalam menghadapi bencana banjir diRW 06 Desa Pasawahan Wilayah Kerja Puskesmas Cicurug Kabupaten Sukabumi. Desainpenelitian menggunakan korelasional dengan cross-sectional. Populasi dalam penelitianini adalah seluruh masyarakat RW 06 Desa Pasawahan Wilayah Kerja PuskesmasCicurug Kabupaten Sukabumi dengan sampel 317 responden melalui proposionalrandom sampling. Teknik pengumpulan data menggunakan kuesioner. Analisis datayang digunakan adalah regresi linier sederhana. Sebagian besar responden memilikimodal sosial kategori sedang dan kesiapsiagaan kategori siap dengan p-value 0,000 yangberarti <0,05 bahwa terdapat pengaruh modal sosial terhadap kesiapsiagaan. Kesimpulanterdapat pengaruh modal sosial terhadap kesiapsiagaan masyarakat dalam menghadapibencana banjir di RW 06 Desa Pasawahan Wilayah Kerja Puskesmas Cicurug KabupatenSukabumi. Disarankan kepada Desa Pasawahan untuk melakukan penyuluhan danpelatihan tentang kesiapsiagaan agar masyarakat siap untuk menghadapi bencana
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".