Dampak Peningkatan Kualitas Permukiman Kumuh Perdesaan terhadap Pengurangan Peluang Kejadian Bencana: Kasus Kebijakan Dana Desa di Indonesia
Bibliographic record
Abstract
Penelitian ini mencoba melihat dampak dari peningkatan kualitas permukiman kumuh terhadap pengurangan kejadian bencana banjir dan tanah longsor di tingkat desa di Indonesia. Dengan menggunakan pendekatan difference-in-differences (DID) pada model regresi logit, penelitian ini menganalisis pengaruh kebijakan dana desa di 24.343 desa di Indonesia dalam kurun waktu 2006-2018. Hasilnya menunjukkan bahwa setelah kebijakan dana desa diimplementasikan, peluang terjadinya kejadian bencana pada kelompok treatment, yaitu desa-desa yang memiliki tingkat permukiman kumuh relatif tinggi adalah 0,761 kali lebih rendah dibandingkan dengan kelompok kontrol, yaitu desa-desa yang memiliki tingkat permukiman kumuh relatif rendah dengan tingkat signifikansi 1%. Hal ini menunjukkan bahwa pembangunan dan atau peningkatan kualitas infrastruktur di permukiman kumuh perdesaan memiliki efek yang signifikan dalam mengurangi peluang terjadinya kejadian 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".