Influence of Local Tax and Zakat Infaq Maslahah Through to Regional Income (Overview of New Trends in Sustainable Development)
Bibliographic record
Abstract
Conditions Batam Island, Indonesia, economic growth declined from 5.4% in 2016, to under 2% percent in 2017, the disparity is difficult to increase revenue growth in Batam. It is necessary to look beyond the local revenues of local taxes, such as zakat and donation, to contribute to Maslahah through local revenue. Which research aimed at contributing to the Regional Income and Maslahah by using samples taken from the Department of Revenue at Batam City, Amil Zakat Agency (BAZ), Indonesia Religious Leader (MUI), Public Welfare with respondents 190. This study used software AMOS version 23 with Structural Equation Modeling (SEM). The result shows that the variable contribution of local taxes to regional variable income is a significant positive contribution to variable regional Infaq variable income is not notable positive. Tithe variable contribution towards regional variable pay is a significant positive contribution of the variable to variable Maslahah local tax is not significant positive contribution of geographical variables to variable Maslahah income is not a significant negative contribution to mutable Maslahah title variable is significantly positive, Infaq variable contribution towards Maslahah variable is significantly positive, regional changes in contributions by local income tax, donation, charity amounted to 55.2%, a shift Maslahah given by local tax contribution, Infaq, welfare, and regional income amounted to 53.6%. For the local contribution, Maslahah significant positive income to the charity and donation should be maximized not a tax.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".