MétaCan
Menu
Back to cohort
Record W2526939136 · doi:10.5539/jpl.v9n8p24

The Strict Criminal Liability in Iran’s Criminal Legal System

2016· article· en· W2526939136 on OpenAlexvenueno aff
Seyyed Mahmoud Mir-Khalili, Abuzar Salarifar

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
Fundersnot available
KeywordsMens reaChequeLegislatorLawStrict liabilityCriminal procedureInstitutionPolitical scienceCriminal lawCriminal liabilityLiabilityCriminologyBusinessSociologyLegislationComputer securityComputer science

Abstract

fetched live from OpenAlex

The strict criminal liability is one of the institutions which has been accepted and expanded its dimensions in countries, including England, since 19<sup>th</sup> century. Although, criminal intent or fault is the most important elements of every offenses, but, due to some reasons, such as expediency, necessity, benefit, increasing prevention index, the mens rea element of offense, fully or partially, is sometimes removed or presumed. In Iran’s criminal legal system, this legal institution has not been considered formally and explicitly by the legislator, but recently, Iranian legal experts, given some existing necessities, try to put some offenses into the category of the strict criminal liability. In fact, some offencesin criminal legal system, namely traffic offences, bounced cheque, injuries resulted from medical operations and some environmental offences, can be placed under the title of the strict criminal liability. Despite many problems to which the acceptance and development of the strict criminal liability may be faced, It seems, in many cases, this institution can solve the problems related to difficulties of proof and ascertainment of the criminal intent beforecourts. Also, today, Iranian judicial courts don’t take into account the mens rea practically and presume itin many cases.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.046
GPT teacher head0.326
Teacher spread0.280 · 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 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

Explore more

Same venueJournal of Politics and LawSame topicTorture, Ethics, and LawFrench-language works237,207