Literature Review Terhadap Kewajiban Moral, Kesadaran, Pengetahuan, Dimensi Keadilan, Ketegasan Sanksi Dan Pelayanan Pajak Yang Mempengaruhi Kepatuhan Pelaporan Wajib Pajak
Bibliographic record
Abstract
The literature review aims to improve the views of the previous authors and complement them with taxation practices in the field related to moral obligations, taxpayer awareness, knowledge/understanding of taxation from the taxpayer side, and from the tax authority side to determine the dimensions of tax justice, the firmness of tax sanctions and the service quality of the tax authorities towards compliance corporate taxpayer reporting. The research process includes the collectibility of published journals, identification and analysis of research results related to taxpayer compliance and other influencing factors, through Google Scholar. The results of the study are taxpayer compliance can be improved through improving information technology, the use of tax funds can be enjoyed by the wider community, involving the world of education, professional institutions, a sense of justice felt by the tax community, fair reciprocal relations between taxpayers and tax officials, availability taxpayer funds. Recommendations from the research are providing incentives (rewards) to obedient taxpayers, reducing or eliminating multiple interpretations of the implementation of tax regulations, issuing rules that have a sense of justice and involving stakeholders from the Directorate General of Taxes.
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.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".