Analisa Atas Besaran Underground Economy di Indonesia Pada Tahun 2016-2021 Dengan Pendekatan Moneter
Bibliographic record
Abstract
This study aims to analyze and determine the magnitude of the value of underground economy activities in Indonesia in the first quarter of 2015 to the fourth quarter of 2021. The method in this study is quantitative analysis using multiple linear regression equations estimated by the least squares method (Ordinary Least Square). The regression equation model used is a monetary approach model, which is a measurement of the magnitude of the underground economy based on the sensitivity of demand for currency. The bound variables used are the amount of demand for currency while the free variables include tax burden, opportunity costs, inflation, nominal Gross Domestic Product (GDP) and financial innovation. The result of this study is that the value of underground economy activities in the research period shows fluctuating values, with a range of values ranging from 127,384 billion to Rp. 625,919 billion The average calculation in that period shows a value that reaches Rp. 325,127 billion or equivalent to 18.2% of Nominal GDP. The novelty of this study is to update the data until 2021 and make adjustments to the variables and models used by the previous study.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".