The Justice of Tax Amnesty and Tax Compliance: Empirical Study in Indonesia
Bibliographic record
Abstract
The purpose of this article is to examine the effect of perceptions of justice in tax amnesties on post-amnesty tax compliance. The independent variables employed were distributive justice, procedural justice, and retributive justice, and the dependent variable is tax compliance. Measurement of the variables was based on five Likert scales, from (1) Strongly Disagree to (5) Strongly Agree. The results of the multiple regression analysis of 133 questionnaire answers indicate that tax amnesty justice has a significant positive effect on post-amnesty tax compliance. The variables of tax justice that have a positive effect on tax compliance are procedural and retributive justice, with regression coefficients of 0.248 and 0.237 respectively, at a significance level of 0.00. A fairer tax amnesty policy will improve tax compliance after the amnesty period. The tax authorities need to make improvements to create a more equitable tax amnesty policy in the future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".