The Importance of Zakat Distribution and Urban-Rural Poverty Incidence among Muallaf (New Convert)
Bibliographic record
Abstract
This study demonstrates literately the importance of regional (urban-rural) differences and the impact of zakatdistribution towards the poverty alleviations among asnaf Muallaf. This study was motivated by the monthlyofficial zakat distribution towards asnaf Muallaf that did not differentiate the true urban-rural cost of livingdifferences. Differences in urban and rural poverty lines that did not reflect the actual urban-rural cost of livingdifferences will provide a misleading picture of the distributional impact of development, which usually involvesurban sector expansion. In a dualistic economy, one of the ways the poor benefit from development is throughexpansion of job opportunities in the modern predominantly urban sector, in addition to increases in productivityin the traditional predominantly rural sector. Thus, this study aims to identify the dispersal of Muallaf in terms ofurban-rural poverty that involved in allocation of zakat distribution. In defining poverty between urban and ruralareas, therefore, one should ensure the differences in the cost of living between these two areas. The findingsuggest that, a huge of amount of zakat been allocated to the development of asnaf Muallaf in the state ofSelangor especially in urban area. However, most of zakat distribution on Muallaf goes to urban region whichhigher in cost of living without considering a set of prices including a broader bundle of goods and servicesrepresentative of the purchases of consumers in different region.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".