Analisis Pengaruh Kredit Usaha Rakyat Sektor Pertanian Terhadap Pertumbuhan Ekonomi dan Penurunan Kemiskinan di Indonesia
Bibliographic record
Abstract
As one of government program, Micro Credit Program, called KUR, is launched to tackle the capital problem faced by Small Medium Enterprises (SMEs) in various business sectors in Indonesia. One of targeted sector by KUR is agricultural sector. This study analyzed the effect of KUR given to the agricultural sector on the economic growth both in the agricultural sector and the aggregate economy in Indonesia. In addition, this study also considered the relationship between the KUR given to the agricultural sector on the poverty reduction in Indonesia in whole as well as in rural areas. Using panel data set of 34 provinces in Indonesia in 2010-2017 and applying Panel-Vector Autoregressive method, the results shows that the KUR given to the agricultural sector had a significantly positive impact on economic growth in agricultural sector as much 0.025%, but insignificant in aggregate. Furthermore, KUR had an impact on the aggregate poverty reduction 0.017% in Indonesia, but insignificant in rural area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".