Bibliographic record
Abstract
This study aims to analyze and determine the effect of interest rates and GDP on the demand for money in contemporary Islamic society. This type of research is descriptive and associative. While the type of data is documentary data, the data source is secondary data and time series data from the first quarter of 2015 to the fourth quarter of 2018. The analysis tool is a simultaneous equation model using the Two Stages Least Squared (TSLS) method. The results of the study conclude that the existing estimates explain that interest rates do not have a significant effect on the demand for money. This fact supports the initial hypothesis which states that there is no effect of interest rates in motivating a contemporary society to hold money in Indonesia. What has a significant effect on the demand for real money based on estimates, is that the level of income in accordance with the theory of income levels will have a positive effect on the demand for money. Based on the results of the research conducted, suggestions can be given, namely, even though the results show that there is no significant relationship between the interest rate and the demand for money, it is not necessarily the insignificant effect of the interest rate because most Indonesian people embrace Islam and have good awareness. against the prohibition of interest, but it could also be due to the high rate of profit sharing from Islamic banks. So that there is no such public attitude, we still need to promote Islamic banking products, and a good explanation about the prohibition of interest. In addition, the improvement of Islamic banking services is also very important in this regard.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.024 | 0.004 |
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".