MEMBANGUN PESANTREN TANGGUH BENCANA MELALUI LITERASI KEBENCANAAN BERBASIS PARTISIPATORY MAPPING DI KOTA MALANG
Bibliographic record
Abstract
Kota Malang merupakan salah satu kota di Jawa Timur yang rawan terjadi bencana. Kerentanan bencana di Malang tidak hanya terjadi di area rural dan urban tetapi juga menimpa komunitas dan institusi sosial tertentu. Komunitas di pesantren sebagai institusi pendidikan berpotensi besar mengalami dan terdampak bencana karena berada pada kawasan yang rawan. Salah satunya yaitu Pondok Pesantren Al-Furqon di Kota Malang yang berada di pinggir Sungai besar tepatnya pertemuan antara Sungai Bango dengan Sungai Brantas. Penelitian ini bertujuan untuk meningkatkan Kesiapsiagaan Pondok Pesantren dalam menghadapi ancaman bencana melalui Literasi Bencana berupa tiga langkah, yaitu 1. Participatory Mapping, 2. Ngaji Bencana, 3. Simulasi Bencana. Dalam penelitian ini juga dilakukan pengukuran wawasan kebencanaan melalui pemberian soal pretest dan posttest. Hasil dari uji T Test menunjukan terdapat peningkatan wawasan kebencanaan sebelum dan sesudah diberikan Literasi Kebencanaan dengan nilai rata-rata sebesar 62 dari sebelumnya nilai rata-rata sebesar 43, selain hal tersebut santri serta pengurus Pondok Pesantren Al-Furqon telah meningkatkan kesiapsiagaan dengan melaksanakan simulasi bencana di lingkungan Pondok Pesantren. Pendekatan participatory mapping dapat meningkatkan literasi dan partisipasi aktif civitas pesantren dalam mengantisipasi bencana.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".