PENGARUH INTERMEDIASI PERBANKAN TERHADAP PERTUMBUHAN EKONOMI INDONESIA
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".