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Record W4394869828 · doi:10.19184/ijabah.v2i1.764

Pengaruh Zakat, Infak, dan Sedekah (ZIS) dan Pembiayaan Perbankan Syariah Terhadap Perekonomian di Indonesia Tahun 2012 - 2022

2024· article· en· W4394869828 on OpenAlexaboutno aff
Mochammad Cholil, Moehammad Fathorrazi, Lilis Yuliati

Bibliographic record

VenueIJABAH · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsGross domestic productQuarter (Canadian coin)EconomicsProduct (mathematics)ShariaBusinessEconomic growthIslamGeographyMathematics

Abstract

fetched live from OpenAlex

This research aims to examine and analyze the influence of Zakat, Infaq and Alms (ZIS) and sharia banking financing on the economy in Indonesia, both in the short and long term. The data in this research is secondary time series data in the form of quarters from 2012 to 2022. The analysis in this research uses VAR/VECM estimation with independent variables namely Zakat, Infaq and Alms (ZIS) and sharia banking financing while the dependent variable is Gross Domestic Product (GDP) Indonesia. The VECM estimation results in this research show that in the short term Zakat, Infaq and Alms (ZIS) during the past 3 quarters had a positive and significant effect on Indonesia's Gross Domestic Product (GDP), then in the long term Zakat, Infaq and Alms (ZIS) during 1 quarter ago had a positive and significant impact on Indonesia's Gross Domestic Product (GDP). Meanwhile, sharia banking financing (IBF) in the short term did not have a significant effect on Indonesia's Gross Domestic Product (GDP), while in the long term sharia banking financing (IBF) 1 quarter ago had a positive and significant effect on Indonesia's Gross Domestic Product (GDP).

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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