BIMBINGAN SATUAN PENDIDIKAN AMAN BENCANA BAGI GURU DAN TENAGA KEPENDIDIKAN PASCA BENCANA DI KOTA PALU, SIGI DAN DONGGALA
Bibliographic record
Abstract
Indonesia adalah salah satu negara dengan potensi alam yang melimpah Meskipun demikian Inonesia memiliki potensi bencana alam tertinggi di Dunia. Salah satu aspek kehidupan yang rentan bencana adlalah lembaga pendidikan. Dalam bidang pendidikan pemerintah menerapkan program pendidikan aman bencana yang dikenal dengan Satuan Pendidikan Aman Bencana (SPAB). Tujuan penelitian ini adalah untuk meningkatkan pemahaman dan kemampuan lembaga pendidikan di Palu dan Sigiuntuk menerapkan Satuan Pendidikan Aman Bencana. Untuk mewujudkan pendidikan tangguh bencana terdapat tiga komponen utama, yaitu pertama; fasilitas sekolah aman. kedua; manajemen Bencana di Sekolah. Dan ketiga; pendidikan pencegahan danpengurangan resiko bencana. Pendekatan yang digunakan penelitian ini adalaheducational approach atau pendekatan edukasi, serta pendekatan partisipatori.Adapun metode penelitian adalah mixed method. Berdasarkan hasil penelitian. Ratarata pemahaman tentang penerapan Satuan Pendidikan Aman Bencana di kota Palu dan Kabupaten Sigi sangat rendah. Hal tersebut terlihat dari hasil pre tes di atas 90%. Akan tetapi setelah kegiatan bimbingan pemahaman responden meningkat secara signifikan yaitu di kategori 100%. Secara analisis kualitatif dapat disimpulkan bahwa kemampuan penerapan SPAB sangat baik.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".