MétaCan
Menu
Back to cohort
Record W1785985788 · doi:10.5539/ass.v11n21p355

The Effectiveness of Zakat in Reducing Poverty Incident: An Analysis in Kelantan, Malaysia

2015· article· en· W1785985788 on OpenAlexvenueno aff
Ahmad Fahme Mohd Ali, Zakariah Abd. Rashid, Fuadah Joharı, Muhammad Ridhwan Ab. Aziz

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyIndex (typography)WelfareContext (archaeology)Distribution (mathematics)PopulationInequalitySocioeconomicsEconomicsDemographic economicsDevelopment economicsEconomic growthGeographyDemographySociologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.256
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicIslamic Finance and Banking StudiesFrench-language works237,207