Zakat Restrictions: Religious, Social, Institutional, and Political. Case Study: Qatar
Bibliographic record
Abstract
Since the early of Islam, zakat - compulsory Islamic almsgiving - has received widespread interest in religious, social, institutional as well as political fields. Zakat distribution, theoretically, comes in the form of providing eight beneficiaries through projects of religious, educational, and medical foundations, provision of military weapons, and so on. However, the annual estimates of zakat indicate that zakat is exposed to factors that restrict its mechanism (collections and distributions). A combination of the theoretical frameworks of the sociology of religion/Islam and political sociology helps to discover and understand these factors. The quantitative method in this research through the interviews conducted with Muslim scholars, charitable organisations’ staff, and Qatari Muslims (citizens and residents), show that zakat distribution is affected by Muslim scholars’ interpretations of ‘for the cause of Allah’ zakat beneficiary, the behaviour of zakat payers in paying individually and conditional zakat projects, Qatar’s tribal culture (citizens), family bonding (residents), the behaviour of charitable organisations in promoting specific projects, and interests of the Qatari state. Furthermore, most participants agreed that the purpose of zakat is to support the less fortunate, rather than personal interests, conflicts, or wars. Any defect in zakat applications is considered a significant loss, especially since the world witnessed a rise in zakat beneficiaries such as the poor and refugees, whether they result from natural or war disasters.
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".