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Record W1503607525

ELASTISITAS SUKU BUNGA TERHADAP PENYALURAN KREDIT INVESTASI PERSEKTOR EKONOMI DI KALIMANTAN BARAT

2013· article· id· W1503607525 on OpenAlexaboutno aff
Bilqis Salbiyyah Fitriyani

Bibliographic record

VenueJurnal Curvanomic · 2013
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInterest rateLoanInvestment (military)AgricultureAgricultural economicsQuarter (Canadian coin)DisbursementBusinessPrice elasticity of demandSimple linear regressionSecondary sector of the economyEconomicsEconomyRegression analysisFinanceGeographyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The title of the study is Elasticity Of Interest Investment Loan Disbursement Economic Per sector in West This study aims to examine and analyze the effect of interest rates on investment loans per sector Economics in West Kalimantan and to determine the level of the interest rate elasticity of investment lending per sector economies in West Kalimantan. This study used time series data between 2007 first quarter to 2011 fourth quarter, which is a secondary data from Bank Indonesia at Banking Statistics of Indonesia. The research method using Simple Linear Regression and elasticity test. Based on the estimates, the study found that the economic sector in West Kalimantan are significant in the mining, industrial, Electricity, Gas, Water, Building, commerce, transport, services and others. While the agriculture sector is not significantly affected. Then the sectors of the economy in western Borneo is Elastic contained in the mining, industrial, Electricity, Gas, Water, Building, commerce, transport, services and others. As well as the agricultural sector is Inelastic. Keywords : Interest Rates, Economic Persector Distribution of Investment Credit in West Kalimantan.

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)0.000
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.014

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 head0.012
GPT teacher head0.187
Teacher spread0.175 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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