The Effectiveness of Zakat in Reducing Poverty Incident: An Analysis in Kelantan, Malaysia
Bibliographic record
Abstract
This study attempts to examine the effectiveness of monthly zakat distribution as a mechanism to poverty reduction in the state of Kelantan. The target population of this study is the masakin (poor) and fuqara (hardcore poor) (Note 1) categories of the Kelantan Islamic Religious Department (MAIK) (Note 2) zakat recipients. Simple Random sampling procedure is applied to collect primary data related to zakat recipients from the poor and hardcore poor category of ten districts of Kelantan. The effects of zakat distribution on poverty are analyzed within the context of burden of poverty; specifically in terms of incidence, intensity and severity of poverty. These are examined using four major indices of poverty, which include the Headcount Index, Average Poverty Gap, Income Gap and Sen Index. Results revealed that zakat distribution reduces poverty incidence, reduces the extent of poverty and lessens the severity of poverty. Further, the current zakat distribution in Kelantan only gives a small effect on increasing the income of the poor. Hence, by locating the perfect amount of zakat distribution to eliminate poverty and to offer alternative zakat distribution model is the best way in reducing the income inequality and maximization of social welfare.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".