Sustainability Strategy to Alleviate Poverty Through Education, Energy, GRDP, and Special Funds: Evidence from Indonesia
Bibliographic record
Abstract
This study discusses the approach to poverty alleviation that occurs in Indonesia, reviewed by using four variables that match the situation. The simulation based on our approach model applies an integrated and multidimensional approach that combines elements of various approaches to alleviating the poverty. This research uses cross-sectional data from 501 regencies and cities the Republic of Indonesia. The data is analyzed using OLS multiple regression with robustness. In addition, this study offers policies for the government to design, manage, and implement poverty alleviation programs. This study enriches the poverty alleviation literature in knowledge capture and sample adequacy. The findings of this study indicate that not all selected independent variables affect the poverty. There are four variables studied in this study, namely, literacy, electricity energy, and GDRP with oil. From the four variables, only three significantly affect the poverty as dependent variable. The most surprising thing is that the special allocation fund variable has an expected sign on its coefficient contrary to the hypothesis. Therefore, the special allocation fund does not support the poverty alleviation throughout the cities and districts in Indonesia. Findings of this study confirm, to some extent, the complementarity of the independent variable to the dependent variable and various approaches to poverty alleviation that need to be employed comprehensively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".