Revamping Zakat Distribution: Asnaf Empowerment Through Conditional Cash Transfer
Bibliographic record
Abstract
Although zakat distribution initiatives continue to evolve with increased funding, the effectiveness of current mechanisms remains low, primarily due to challenges in fostering selfreliance among asnaf (zakat recipients), who remain dependent on zakat.To tackle this, a revamp in zakat distribution is necessary to transform behaviour and eliminate poverty.This study aims to discuss the conceptual framework of Conditional Cash Transfers (CCT) in zakat distribution, a global poverty alleviation program that promotes behaviour change through conditional interventions.A systematic review of 30 selected articles identified seven key CCT impacts: education, health, employment, poverty, crime, environment, and food security.The findings highlight the diverse impacts of CCT programs.To strengthen CCT within zakat distribution, the study focuses on incorporating religious behavioural conditions to ensure effective outcomes, aligning with the goal of Al-falah.The discussion also addresses the challenges in CCT development and provides solutions based on the literature.This research can contribute valuable insights to policymakers and researchers on poverty alleviation models that tackle behavioural issues among asnaf, and suggests the development of religious behavioural conditions to create a robust, effective, and sustainable CCT program model.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".