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Record W2316001506 · doi:10.5539/jpl.v9n2p96

An Investigation into the Rule of Action in the Penal System

2016· article· en· W2316001506 on OpenAlexvenueno aff
Sahar Zadnahal, Iraj Goldozian

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAction (physics)Compensation (psychology)Punishment (psychology)LawRelation (database)LiabilityPolitical scienceCausationCriminal lawCriminal liabilityStrict liabilityLaw and economicsTortCausality (physics)PsychologySociologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

According to definite jurisprudential principles and rules, when a person acts against himself and accepts the incurrence of damage, he will be deprived of all or some part of the claim for compensation. The rule of action constitutes the basis of this lack of benefit in Islamic and Iran’s law, while the principles of consent and participation in the fault underlie it in the common law. Studying research studies conducted in relation to the rule of action in Iran and Britain’s law it could be argued that the rule of action is among the cases discussed in criminal law and on the issue of causality in punishment. On the other hand, in criminal proceedings, in Iranian and British legal systems, this issue has been mostly discussed regarding the aspects of imposing damage and calculation of costs thereof. Therefore, if someone is acting to the detriment of himself and is causing damage to himself, in addition to the issue of liability removal, in which the accused may be acquitted from his responsibility, there is no need for the accused person to pay compensation to the injured person.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.023
Scholarly communication0.0130.007
Open science0.0010.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.075
GPT teacher head0.437
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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