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Record W1519222000

ANALISIS JUMLAH UANG BEREDAR M2 DAN TINGKAT SUKU BUNGA TERHADAP PERTUMBUHAN EKONOMI DI KALIMANTAN BARAT

2013· article· id· W1519222000 on OpenAlexaboutno aff
B Syafari

Bibliographic record

VenueJurnal Curvanomic · 2013
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMoney supplyInterest rateEconomicsQuarter (Canadian coin)Regression analysisIncome elasticity of demandEconometricsElasticity (physics)VariablesAgricultural economicsMonetary economicsMathematicsStatisticsGeography
DOInot available

Abstract

fetched live from OpenAlex

The title of the study is Supply Analysis and Growth Rate Against West This study aimed to determine the effect of the money supply and interest to economic growth western Kalimantan and analyze the elasticity of the money supply and interest rates to economic growth western Borneo. This study uses time series data since 2001 first quarter to 2010 fourth quarter, which is the data from the Central Bureau of Statistics and Bank Indonesia. This study using correlative and regression analysis using eviews program. Based on the estimates, the study found that the variables that affect the amount of money supply and interest rates have a very strong relationship to economic growth. Money supply variables significantly influence economic growth and interest rates that significantly influence economic growth. Test results obtained from the elasticity of the elasticity of the money supply and interest rates to economic growth is elastic West Kalimantan. Based on the classic assumption test found that the estimates of the regression model containing positive autocorrelation, does not contain muktikolinearitas, and contains no heterocedastisity. Keywords : Keywords: Economic Growth, Money Supply and Interest Rates.

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, 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 score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.023
GPT teacher head0.210
Teacher spread0.187 · 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
Published2013
Admission routes1
Has abstractyes

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