Dampak Pertumbuhan Sektoral terhadap Ketimpangan Pendapatan dan Kemiskinan di Indonesia:Analisis menggunakan Social Accounting Matrix dan Micro-Simulation
Bibliographic record
Abstract
Berdasarkan Social Accounting Matrix (SAM) Indonesia 2015 yang diperluas dan dengan menggunakan metode micro-simulation, penelitian ini menunjukkan bagaimana pertumbuhan sektoral mempengaruhi ketimpangan pendapatan dan kemiskinan di Indonesia serta kontribusi Usaha Kecil dan Menengah (UKM) sektor manufaktur dalam mengurangi ketimpangan pendapatan. Dari 24 sektor di Indonesia, hanya pertumbuhan pada 10 sektor ekonomi yang dapat mengurangi ketimpangan pendapatan, dengan penurunan terbesar pada pertumbuhan sektor tanaman pertanian lainnya. Di sisi lain, UKM tidak memiliki pengaruh penting dalam mengurangi ketimpangan pendapatan, yang berpengaruh adalah dimana sektor UKM itu berada. Penelitian ini juga menunjukkan bahwa pertumbuhan di semua sektor ekonomi berdampak pada pengentasan kemiskinan dengan kontribusi yang berbeda. Pertumbuhan sektor tanaman pertanian lainnya memberikan kontribusi terbesar dalam pengentasan kemiskinan di Indonesia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".