MétaCan
Menu
Back to cohort
Record W2940045534 · doi:10.35448/jte.v12i1.4443

PENGARUH INTERMEDIASI PERBANKAN TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2017· article· id· W2940045534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTirtayasa Ekonomika · 2017
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDistributed lagCointegrationEconomicsError correction modelShock (circulatory)Quarter (Canadian coin)Real gross domestic productMonetary economicsEconometricsTerm (time)VariablesAutoregressive modelFinancial intermediaryControl variableFinancial systemStatisticsMathematicsGeography

Abstract

fetched live from OpenAlex

This study aims to discuss banking as a financial intermediary institution in increasing economic growth. The banking intermediation variable in this study is measured by two variables, namely the ratio of credits per Real GDP and the ratio of third party funds to Real GDP. In addition to financial variables, also used control variables to economic growth is BI-rates. The data used are 1 st quarter 2007 to 4 th quarter 2014. This study uses a cointegration test of the Autoregressive Distributed Lag (ARDL) approach to prove the long-term effects between variables and error correction models (ECM) to see how quickly the economy returns to a balanced state when there is a short-term shock. The result shows that there is a long-term relationship between variables, where the ratio of credits per Real GDP, third party funds to Real GDP, and BI- rates have a positive and significant impact on Indonesia's economic growth, both in the long term and short term

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.

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), Science and technology studies, 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 score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0000.001
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 head0.021
GPT teacher head0.216
Teacher spread0.196 · 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