Optimalisasi Penerapan Penyanderaan (Gijzeling) sebagai Upaya Penegakan Hukum (Law Enforcement) dalam Penerimaan Pajak (Studi Kasus Pelaksanaan Penyanderaan Di Kantor Wilayah Direktorat Jenderal Pajak Sumatera Utara I)
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
This research focuses on Optimizing the Implementation of Hostage Taking (Gijzeling) as an Effort to Enforce Law in Tax Revenue (Case Study of Taxpayer Hostage Taking in Regional Office I of the Directorate General of Taxes, North Sumatra Province). The research method used in this study is normative legal research or doctrinal legal research. This research is descriptive with qualitative data analysis methods. The results of the study indicate that the hostage taking (Gijzeling) at the Regional Office I of the Directorate General of Taxes of North Sumatra Province has been carried out in accordance with statutory regulations, namely the Provision of Compensation in terms of Tax Collection by Warrant juncto Regulation of the Directorate General of Taxes Number PER-03/PJ/ 2018 concerning Amendment to the Decree of the Directorate General of Taxes Number KEP-218/PJ/2003 concerning Guidelines for Hostage Taking and Rehabilitation of the Reputation of Taxpayers Hostage. Tax bearers who have been taken as hostages have fully paid their tax debts which have resulted in increased tax revenues and coordination with related agencies so that hostage-taking (gijzeling) is an effort to enforce the law. in optimizing tax revenue can be implemented properly
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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