Dampak Dana Desa terhadap Ketimpangan Desa-Kota di Indonesia
Bibliographic record
Abstract
This study aims to analyze whether the Village Fund (VF), which has been implemented for almost a decade since first implemented in 2015, has been able to reduce rural-urban inequality in Indonesia. Apart from being one of the national development priority agendas, reducing inequality is also one of the Sustainable Development Goals (SDGs) agendas. The research employs secondary data and consists of two main stages of analysis. First, a paired sample t-test was used to test the level of rural-urban inequality in Indonesia before (Pre) and after (Post) Village Fund. Second, the author analyzed the impact of the Village Fund on rural-urban inequality through a linear regression test. The results show that there is a decrease in rural-urban inequality by 0.0335 - 0.0462 after testing the Pre-VF and Post-VF. Meanwhile, using provincial-level panel data from 2016 to 2023, it was found that the decline of rural-urban inequality in Indonesia was affected by the Village Fund policy. It means that the Village Fund as one of the financial incentive policies for village development has an impact on rural-urban inequality reduction in Indonesia, although the impact is still extremely limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".