Tax Amnesties in Indonesia and Other Countries: Opportunities and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it