Tax Amnesties in Indonesia and Other Countries: Opportunities and Challenges
Bibliographic record
Abstract
Recently, revenue of national budget from taxes has decreased since economic deceleration happened and many capitals and assets of Indonesian people were stationed overseas. In order to encourage the economic growth, the government establishes regulations on tax amnesty.This paper primarily aims to find out the implementation of tax amnesty in Indonesia which had run three times since 1964, 1984, and 2016; and to compare it with similar program implemented by several countries such as South Africa, India, and Italy. Tax amnesty program in 1964 and 1984 was considered unsuccessful due to the political condition at that moment and the government indifference to socialize this matter to the taxpayers. However, it differs from South Africa, India, and Italy which are considered successful in implementing the tax amnesty program, because it brings good impact on their national revenue and increased the obedience of the taxpayers. In order to reach the objectives of the tax amnesty program in 2016, Indonesia government needs to revise the regulations of taxation, prepare the human resource of tax officers, to prepare information system related to the data of taxpayers, to improve the coordination of public agencies from Financial Service Authority and Indonesian Financial Transaction Reports and Analysis Center (INTRAC) and to enforce the regulation after the enactment of tax amnesty.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".