Pengaruh Pajak Daerah Dan Retribusi Daerah Terhadap Pendapatan Asli Daerah Kabupaten Sikka Tahun 2023
Bibliographic record
Abstract
An important implication of regional autonomy is fiscal decentralization where regions have the authority to regulate and manage their own affairs including regional financial management but the increasing needs of the regions cause the financing of government and development programs/activities to also be greater so that the Central Government is stricter in matters of Transfers to Regions and autonomous regions including Sikka Regency must be independent through Regional Original Revenue (PAD) but the slow development and public services in Sikka Regency in 2023 due to low PAD caused by low Regional Taxes which only contribute 67.34% and Regional Retributions which only contribute 50.77%. This Policy Paper aims to achieve the target of Local Tax and Local Retribution fulfillment in 2024 and develop policy recommendations to achieve the target. A quantitative approach is used to analyze the percentage of Local Tax and Local Retribution achievements in the PAD Structure of the Sikka Regency APBD in 2023. In-depth interviews with civil servants of the Regional Revenue Agency to identify challenges and perceptions related to the PAD management process were also conducted. The results of the analysis show the low realization of local taxes and levies in Sikka Regency with contributing factors including potential PAD not yet well recorded or fully collected, ineffective local tax and levy collection systems and poorly recorded taxpayer and levy databases. To overcome this, the policy recommendation is the Sikka Regent Regulation on the Technical Guidelines for the implementation of local tax and levy management as well as efforts to pick up the ball on tax and levy collection to the community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".