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Record W4402220546 · doi:10.36985/tahd5f10

Pengaruh Dana Bagi Hasil Provinsi Dan Bantuan Keuangan Pemerintah Provinsi Terhadap Produk Domestik Regional Bruto Kabupaten Simalungun

2019· article· id· W4402220546 on OpenAlexaff
Roy Sartana Napitupulu, Jef Rudiantho Saragih, Galumbang Hutagalung, Ringkop Situmeang

Bibliographic record

VenueJurnal Regional Planning · 2019
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk menganalisis pengaruh parsial DBH Propinsi dan BKP Propinsi terhadap PDRB Kabupaten Simalungun dan pengaruh simultan DBH Propinsi dan BKP Propinsi terhadap PDRB Kabupaten Simalungun. Berdasarkan jenis masalah yang diteliti, pendekatan yang digunakan adalah deskriptif kuantitatif dan verifikatif kuantitatif. Hasil penelitian menunjukkan bahwa Dana Bagi Hasil (DBH) Provinsi dan Bantuan Keuangan (BKP) Provinsi secara simultan berpengaruh signifikan terhadap PDRB Kabupaten Simalungun. Dari hasil uji F (simultan) yang dilakukan, diperoleh Fhitung sebesar 14,358 dimana Ftabel adalah 4,74. Dengan demikian Fhitung (14,358) > Ftabel (4,74), maka DBH dan BKP Provinsi secara simultan berpengaruh signifikan terhadap PDRB Kabupaten Simalungun. Secara parsial Dana Bagi Hasil (DBH) Provinsi berpengaruh signifikan terhadap Pengembangan Wilayah Kabupaten Simalungun. Nilai signifikansi DBH Provinsi diperoleh sebesar 0,03 lebih kecil dari signifikansi Alpha 0,05 yang menunjukkan bahwa DBH Provinsi berpengaruh signifikan terhadap PDRB. Sementara nilai signifikansi BKP Provinsi diperoleh sebesar 0,058 dimana nilai ini lebih besar dari nilai Alpha 0,05, dengan demikian BKP Provinsi secara parsial tidak berpengaruh signifikan terhadap PDRB Kabupaten Simalungun

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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; a candidate call from one teacher head, not a consensus.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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