The impact of economic growth on technological developments, emoneys and fluctuations interest rates and exchange rates in Indonesia
Bibliographic record
Abstract
Technological developments have an impact on the payment system, namely Electronic Money moved very fast in 2018 and 2019, in 2018 it was 50.3% of the money in circulation and economic growth increased by 5.4% even though the interest rate in that year was at 6%. This means that some Indonesians have started to make changes to the payment system. Changes in digitalization in the financial sector, especially with new fundamental changes in the behavior of people's lives from the social and economic fields. The concept of Financial Technology is very good in the formation of digital financial infrastructure based on sustainable technological innovations that are considered effective in financial markets, including for small and medium-sized companies, this article focuses on three factors that affect economic growth, namely capital, labor, and technological developments. This study uses secondary time series data for the 2004-2019 quarter using Multiple Regression (OLS/One Least Square) and processed using the eviews 10 application. This study aims to determine the impact of technology on economic growth. And the results show that Emoney has a negative and significant effect on economic growth, interest rates and exchange rates have a negative effect on economic growth, and technology has a positive effect on economic growth.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".