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Record W4283780850 · doi:10.36476/jirs.7:1.06.2022.11

Concept of Retribution and Blood Money in Islam and Judaism: Similarities and Differences

2022· article· en· W4283780850 on OpenAlexaff
Saleem Nawaz, Muhammad Ali Shaikh

Bibliographic record

VenueJournal of Islamic and Religious Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsRetributive justiceJudaismIslamPunishment (psychology)LawCriminologyPhilosophyPolitical scienceSociologyTheologyPsychologyEconomic JusticeSocial psychology

Abstract

fetched live from OpenAlex

In all Divine religions, the law of retribution was legislated to protect the sanctity and survival of human life, to end crimes and oppression, prevention of chaos and anarchy, and to maintain collective order and peace in the society. In this regard, an analytical and comparative study of the concept of retribution in Islam and Judaism, the types of retribution, the retribution of injuries and damaged limbs, the principles of testimony, the procedure for issuing and implementing punishment and authority of implementation of punishments, has been done in the light of the Old Testament, the Holy Qur'ān and the Aḥādīth. The study concludes that in Judaism, the law of retribution is required in both cases, i.e; intentional murder and unintentional murder and forgiving the murderer and taking blood money is strictly forbidden, while in Islam, the law of retribution is only for intentional murder and the heirs is obliged to take Qiṣāṣ or to take blood money or to forgive the murderer, while in unintentional murder, the heirs can either take blood money or forgive the murderer. In Islam, only government is authorized to execute the retribution, while in Judaism, the heirs of the deceased can also kill the murderer. In both religions, the testimony of more than one person is necessary for the execution of Qiṣāṣ.

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)0.000
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 score0.254
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.220
Teacher spread0.208 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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