The Impact of Regional Educational Development Policies on Poverty Reduction in North Sumatra Province
Bibliographic record
Abstract
This study aims to evaluate the impact of educational development on poverty reduction in districts and cities of North Sumatra Province, Indonesia.The main focus of this study is to assess the influence of education budget realization on the Regional Education Development Index (REDI), the contribution of REDI and GRDP percapita to poverty reduction, and to develop the Regional Development of Education Index (RDEI) as a policy evaluation tool based on education.The methodology employed includes multiple regression analysis with classical assumption tests and the construction of an education index based on the conditional weighted product method.The research results show that 62% of the variation in REDI can be explained by the 20% realization of the education budget, while 62% of the variation in the poverty index at the regional level is explained by REDI, GRDP per capita, and other related variables.Medan City has the highest RDEI score in North Sumatra (67.22), far above the provincial average (52.14),reflecting the excellent educational performance in this area.The main contribution of this study is the development of RDEI, which provides a more comprehensive policy evaluation tool.It can be used to maximize the effectiveness of education budget allocation and promote improvements in educational policies to support regional development and poverty reduction in a more optimal way.This research makes an important contribution to the literature on educational policy and poverty reduction, particularly by integrating education-based index evaluation with regional development policy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".