PENGARUH BELANJA PEMERINTAH TERHADAP PERTUMBUHAN EKONOMI DAN PENGENTASAN KEMISKINAN DI KOTA BITUNG
Bibliographic record
Abstract
PENGARUH BELANJA PEMERINTAH TERHADAP PERTUMBUHAN EKONOMI DAN PENGENTASAN KEMISKINAN DI KOTA BITUNG Yeni Saarce Magdalena Lantu, Rosalina A.M. Koleangan, Tri Oldy RotinsuluEkonomi Pembangunan – Fakultas Ekonomi dan BisnisUniversitas Sam ratulangi ABSTRAKAlokasi belanja dari pemerintah daerah merupakan salah satu indikator percepatan pembangunan yang terjadi di daerah, dimana alokasi belanja ini kemudian dibagi dalam dua kategori utama yakni belanja langsung dan belanja tidak langsung.Melalui penelitian ini akan dibuktikan bagaimana alokasi belanja langsung dan belanja tidak langsung memberi pengaruh terhadap perkembangan perekonomian di kota Bitung yang dilihat dari pertumbuhan ekonomi serta bagaimana pengaruhnya terhadap tingkat kemiskinan yang terjadi. Penelitian ini sendiri akan menggunakan regresi berganda sebanyak dua kali, untuk melihat pengaruh masing-masing variabel independent terhadap masing-masing variabel dependent. Kata kunci: Belanja langsung, Belanja Tidak Langsung, Pertumbuhan Ekonomi, Kemiskinan.ABSTRACT The allocation of expenditure from local government is one of the indicators of the acceleration of development that occurs in the region, where the allocation of expenditure is then divided into two main categories namely direct expenditure and indirect spending.Through this research will be proved how the allocation of direct expenditure and indirect spending gives effect to the economic development in the city of Bitung seen from the economic growth and how its influence on the level of poverty that occurred. This research alone will use multiple regression twice, to see the influence of each independent variable to each dependent variable . Keywords: Direct Expenditure, Indirect Expenditure, Economic Growth, Poverty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".