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Record W3132625930 · doi:10.29103/j-mind.v4i2.3381

FLYPAPER EFFECT PADA PENGARUH DANA ALOKASI UMUM, DANA ALOKASI KHUSUS DAN PENDAPATAN ASLI DAERAH TERHADAP BELANJA DAERAH KABUPATEN/KOTA DI PROVINSI ACEH

2020· article· id· W3132625930 on OpenAlexaff
Nurul Ikhwani, Naz’aina Naz’aina, Ratna Ratna

Bibliographic record

VenueJ-MIND (Jurnal Manajemen Indonesia) · 2020
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk menguji pengaruh dana alokasi umum, dana alokasi khusus dan pendapatan asli daerah terhadap belanja daerah. Data penelitian yang digunakan adalah gabungan antara cross section dengan time series pada 23 Kabupaten/Kota di Provinsi Aceh selama 2013-2018, yang diperoleh dari Laporan Realisasi Anggaran (LRA) yang dikeluarkan oleh Direktorat Jendral Pengelola Keuangan Daerah Kementrian Keuangan (DJPK Kemenkeu). Metode analisis data yang digunakan adalah Regresi Data Panel dengan Bantuan Eviews 10. Hasil penelitian ini membuktikan bahwa dana alokasi umum, dana alokasi khusus dan pendapatan asli daerah berpengaruh positif dan signifikan terhadap belanja daerah. Besarnya pengaruh dana alokasi umum, dana alokasi khusus dan pendapatan asli daerah terhadap belanja daerah adalah sebesar 92,07% dan sisanya sebesar 7,93% dipengaruhi oleh variabel lain diluar model penelitian ini.Penelitian ini juga membuktikan bahwa terjadi flypaper effect pada DAU Kabupaten/Kota di Provinsi Aceh dan pada DAK flypaper effect hanya terjadi di sebagian Kabupaten/Kota di Provinsi Aceh, ini dilihat dari kontribusi DAU dan DAK yang lebih tinggi dibandingkan PAD dalam merespon belanja daerah. Rekomendasi dari hasil penelitian ini yaitu diharapkan pihak pemerintah daerah Kabupaten/Kota di Provinsi Aceh untuk menggunkan dana alokasi umum dan dana alokasi khusus sebaik dan seefektif mungkin, dan diharapkan pihak pemerintah daerah lebih menggali potensi-potensi daerah agar dapat meningkatkan PADKata Kunci : Dana Alokasi Umum, Dana Alokasi Khusus, Pendapatan Asli Daerah, Belanja Daerah

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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 score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.220
Teacher spread0.191 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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