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Record W2487927515 · doi:10.5539/jpl.v9n6p9

Crimes against Benefits of Organs in Fiqh and Penal System of Iran

2016· article· en· W2487927515 on OpenAlexvenueno aff
Jamal Asayeshi, Khalil Afandak

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsObligationFiqhCompensation (psychology)JurisdictionPenal codeIslamLawValue (mathematics)JurisprudenceWork (physics)Law and economicsShariaBusinessPolitical scienceSociologyPsychologyPhilosophySocial psychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Benefits of organs refer to non-material forces put by God in human organs definite or indefinitely e.g. eyesight in the eyes, wisdom in brain, or heart beating. The benefits of internal organs are like the organs outside the body and is under cover of the general rule of whatever there is only one of it in the body, the blood money will be a full one. Many scholars have mentioned the obligation for a full compensation for destruction of each organ. Several studies have been conducted on the elimination of benefits or determination of the value of the organ in religious regulatory books and their evaluation in Islamic jurisdiction and Islamic penal code. This penalty is sometimes called blood money or in other cases compensation. This money is determined by religious codes and should be paid by the criminal for compensating the damage induced by him. Elaboration on these benefits in religious books and then discussing the rules and regulations and finally comparing them with Iranian legal system is the goal followed by the author of the present work.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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
Published2016
Admission routes1
Has abstractyes

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