EVALUATING THE EFFECTIVENESS OF TAX OBJECTION REVIEW IN INDONESIA’S TAX AUTHORITY
Bibliographic record
Abstract
An evaluation of the effectiveness of tax objection review by the Directorate General of Taxes (DGT) is required due to the increasing number of tax disputes that continue to litigation and a low winning rate for DGT in tax court (approximately 40%). This study aims to analyze the effectiveness of reviewing tax objections at DGT using Campbell's Effectiveness Theory (1989) with criteria of programs and goals success, program satisfaction, inputs and outputs conformity, and overall goal achievement. This is case study research with a qualitative method presented in a descriptive analysis. Data was collected through documentation, interviews, and satisfaction surveys. Informants are from the DGT, Taxpayers, Tax Consultants, Tax Lecturers and the Secretariat of the Tax Supervisory Committee (Setkomwasjak). The results indicate that the tax objection review at the DGT has been moderately effective, as evidenced by the achievement of the predetermined targets. However, several criteria should be improved, such as input and output quality, workload and independence. The separation of the objection review unit from the Regional Office (Kanwil) of the DGT is one of the recommendations proposed to increase the independence of tax objection review.
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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 | 0.070 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".