EVALUASI DAMPAK PROGRAM PERCEPATAN PENGHAPUSAN KEMISKINAN EKSTREM DI INDONESIA
Bibliographic record
Abstract
Pemerintah Indonesia menghadapi perlambatan penurunan kemiskinan ekstrem sehingga berusaha mempercepatnya melalui Program Percepatan Penghapusan Kemiskinan Ekstrem (Program Tahap I). Evaluasi dampak Program ini masih terbatas. Oleh karena itu, penelitian ini bertujuan mengevaluasi dampak Program dalam mempercepat penurunan kemiskinan ekstrem. Teori perubahan yang digunakan menjelaskan bahwa bantuan sosial pada Program Tahap I akan meningkatkan konsumsi penduduk miskin ekstrem sehingga bisa keluar dari kemiskinan ekstrem. Dengan menggunakan metode difference in difference, penelitian ini menunjukkan terjadinya penurunan kemiskinan ekstrem di lokasi Program, tetapi penurunan tersebut secara statistik tidak signifikan. Oleh karena itu, disimpulkan bahwa Program Tahap I tidak berdampak dalam percepatan penurunan kemiskinan ekstrem. Hal ini kemungkinan disebabkan oleh bantuan sosial yang tidak tepat sasaran dan nilai bantuan yang diterima sebagian penduduk miskin ekstrem lebih kecil dibanding poverty gapnya. Penelitian ini menyarankan untuk melaksanakan penelitian lebih lanjut guna mengevaluasi dampak program secara lebih komprehensif (Program Tahap I-III), menyediakan data sasaran yang akurat, dan memberikan bantuan sosial sesuai poverty gap.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".