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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".