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PENGARUH BELANJA PEMERINTAH TERHADAP PERTUMBUHAN EKONOMI DAN PENGENTASAN KEMISKINAN DI KOTA BITUNG

2019· article· id· W2789904197 on OpenAlexaff
Yeni Saarce Magdalena Lantu, Rosalina A.M. Koleangan, Tri Oldy Rotinsulu

Bibliographic record

VenueJURNAL PEMBANGUNAN EKONOMI DAN KEUANGAN DAERAH · 2019
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.003

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.

Opus teacher head0.019
GPT teacher head0.213
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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