Zakah Management For Poverty Alleviation In Indonesia And Brunei Darussalam
Bibliographic record
Abstract
Concern for poverty is not new and has been the focus for centuries by historians, sociologists, and economists. The cause has been identified, ranging from shortcomings in the administration of income support, until the injustice of the social and economic system. Various attempts have been proposed, from the reform of social security system for changes in the form of the socio-economic system. Because poverty is a multidimensional problem, solutions to poverty require a set of coordinated action, particularly through charity. Indonesia, which has a population with a large population, of course, the problem of poverty continues to be a problem in economic development. Nevertheless, the potential zakat Indonesia larger community and cooperation among stakeholders and government regulation is a solution to reduce the level of poverty in Indonesia. It is certainly different from the Brunei Darussalam to the level of a small population and large government revenues, management of zakat by MUIB in the form of cash grants, the capital of commerce, and others are implementable can solve the problem of poverty in this country.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".