Assessing Asnaf Readiness for Conditional Cash Transfer Adoption in Malaysia: A Descriptive Analysis
Bibliographic record
Abstract
This study aims to descriptively analyse the perceptions of asnaf regarding their readiness for Conditional Cash Transfer (CCT) adoption in Malaysia.Its originality lies in examining zakat cash recipients' perspectives, representing the first attempt to develop a CCT model within zakat distribution and integrate behavioural conditionalities including religiosity, education, health, and employment.The study surveyed 369 heads of households classified as poor or needy asnaf who received cash assistance from zakat institutions in the Federal Territory, Kedah, and Terengganu, selected to reflect variations in governance structures and zakat distribution practices.Data were analysed using descriptive statistics, frequency analysis, cross-tabulation and Pearson's Chi-square test to check compliance trends across states and asnaf categories.Findings indicate strong overall acceptance of behavioural conditionalities, with the highest compliance observed in religious practices and the greatest challenges in employment.The findings highlight the need for differentiated strategies in zakat-based CCT programs, combining soft conditionalities for generally compliant recipients and hard conditionalities for those requiring stricter enforcement.The study demonstrates the potential of zakat-based CCT models to enhance asnaf compliance, promote human capital and spiritual development, and support socio-economic empowerment and long-term poverty alleviation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".