PEMETAAN PARTISIPATIF ANCAMAN, STRATEGI COPING DAN KESIAPSIAGAAN MASYARAKAT DALAM UPAYA PENGURANGAN RESIKO BENCANA BERBASIS MASYARAKAT DI KECAMATAN SALAM KABUPATEN MAGELANG
Bibliographic record
Abstract
Pasca erupsi Gunungapi Merapi tanggal 26 Oktober 2010, bahaya lahar merupakan ancaman bencana di wilayah Kabupaten Magelang. Kecamatan Salam merupakan daerah rawan bahaya lahar dengan keberadaan empat sungai yang berhulu di Gunungapi Merapi, yaitu Kali Krasak, Kali Batang, Kali Putih dan Kali Blongkeng. Diantara keempat sungai tersebut, Kali Putih merupakan salah satu sungai dengan tingkat kerawanan tinggi. Penelitian ini bertujuan untuk mengidentifikasi karakteristik ancaman, mengidentifikasi strategi koping dan kesiapsiagaan masyarakat dalam mengurangi risiko bencana. Penelitian ini bersifat deskriptif dengan pendekatan kualitatif dan menggunakan metode Participatory Geographic Information System (P-GIS) melalui pemetaan partisipatif dalam kegiatan FGD. Hasil penelitian menunjukkan bahwa pengetahuan dan pengalaman masyarakat dalam menghadapi bencana lahar membentuk pemahaman yang baik untuk meningkatkan kapasitas berupa kesiapsiagaan masyarakat menghadapi ancaman banjir lahar pascaerupsi Merapi 2010. Melalui kegiatan pemetaan partisipatif ancaman dalam pengurangan risiko bencana berbasis masyarakat dapat teridentifikasi daerah rawan lahar, kerentanan, kesiapsiagaan serta teridentifikasinya potensi dan sumber daya yang tersedia yang dapat digunakan masyarakat dalam pengelolaan risiko bencana. Kata kunci : Pemetaan partisipatif, PRB berbasis masyarakat, strategi koping dan kesiapsiagaan
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".