The Impact of Socioeconomic, Government Expenditure and Transportation Infrastructures on Economics Development: The Case of West Timor, Indonesia
Bibliographic record
Abstract
Socioeconomic, government expenditures, and transportation infrastructures, such as roads and ports, play a critical role in the economic development of poor and deprived regions. These factors are as catalysts for increasing economic output and enhancing people’s living standards in these regions. This study of West Timor, Indonesia, aimed to examine the impact of Socioeconomic, government expenditure, road, and port infrastructure on economic growth. This study analyzed panel data from a cross-section of six regencies/municipality in the West Timor region from 2002 to 2020. The analytical method used in this research is the fixed effect model (FEM) and the random effect model (REM). We found that human development, government expenditure on personnel and capital expenditure, urbanization, and district roads positively impacted economic growth, while expenditure on goods and services has a negative impact on economic growth. In contrast to district roads, national roads adversely impact economic growth due to triggering backwash and leakage of the regional economy. Therefore, human development, government expenditure, and infrastructure development to encourage the local economy of underdeveloped areas in the country’s border areas must consider the needs of local communities in increasing their productivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".