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Pengaruh Dana Alokasi Umum, Dana Alokasi Khusus dan Pendapatan Asli Daerah Terhadap Belanja Daerah Pada Kabupaten Padang Pariaman 2010-2017

2019· article· id· W3152727381 on OpenAlexaboutno aff
Robi Wahyu Saputra, Joni Fernandes

Bibliographic record

VenueFigshare · 2019
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueQuarter (Canadian coin)Local governmentGovernment (linguistics)BusinessAgency (philosophy)FinanceEconomicsGeographyPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

The purpose of this\nstudy is to examine the effect of general allocation funds, special allocation\nfunds and Original\nLocal Government Revenues on regional expenditure in the Padang Pariaman\nDistrict Government in West Sumatra Province in the first quarter of 2010 - IV\nquarter 2017. The object of this research is the Padang Pariaman District\nGovernment. The data used is secondary data taken from the report on the\nrealization of the regional revenue and expenditure budget of Padang Pariaman\nRegency obtained from the Regional Financial Management Agency (BPKD). The results of the study\nshow that the Special Allocation Funds and Original Local Government Revenues have a significant effect on regional expenditure, while\nthe General Allocation Funds does not have a significant effect on the Regional\nExpenditure of the Government of Padang Pariaman Regency, West Sumatra Province

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.053
GPT teacher head0.225
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2019
Admission routes1
Has abstractyes

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