IMPLEMENTASI ZAKAT CORE PRINCIPLES DALAM PENDISTRIBUSIAN ZAKAT DI BAITUL MAL KOTA BANDA ACEH
Bibliographic record
Abstract
This study aims to investigate the implementation of the Zakat Core Principles (ZCP) in the distribution of zakat at Baitul Mal Kota (BMK) Banda Aceh, which includes; determining the distribution to mustahik, determining the distribution area of zakat, and the performance of zakat distribution based on ratio indicators and disbursement time. This research is a qualitative descriptive study using interviews and documents/archives related to the implementation of zakat distribution. The results showed that Baitul Mal Kota Banda Aceh has implemented the Zakat Core Principles in determining the distribution of zakat to mustahik, and determining the area of zakat distribution. The performance of zakat distribution is based on the calculation of the Zakat Core Principles, the average disbursement of zakat funds through the Disbursement to Collection Ratio (DCR) showed a value exceeding 90% (included in the very effective category), the disbursement of zakat funds for consumptive programs is carried out once every quarter (including in the good category), while for productive programs every 12 months or once a year (included in the good category). It is hoped that the Zakat Core Principles will be implemented comprehensively at Baitul Mal Kota Banda Aceh in the future.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".