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Record W2911246035 · doi:10.5539/jpl.v12n1p20

The Penal Liability of the Moral Person in the Electoral Publicity Crimes in the Jordanian House of Representatives Electoral Law No. 6 of 2016

2019· article· en· W2911246035 on OpenAlexvenueno aff
Wejdan Suleiman Irtaimeh

Bibliographic record

VenueJournal of Politics and Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Election Politics and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsPublicityLawLegislatorPolitical scienceLegislatureLiabilityLegislation

Abstract

fetched live from OpenAlex

This study aims to clarify the provisions of the liability of the moral person in the electoral publicity crimes in the Jordanian House of Representatives Electoral Law No. 6 of 2016, and to clarify the problems of implementing the criminal sentences issued against him. The study indicated that the Jordanian legislator did not stipulate any substantive or procedural rules concerning the penal liability of the moral person for the electoral crimes in the House of Representatives Electoral Law No. 6 of 2016 relying on the general rule stated in Article 74 of the Jordanian Penal Code No. 16 of the year 1960 and its amendments, which established the penal liability of the private moral persons and excluded the public moral persons i.e. the governmental department or official or public institution. The study concluded that the liability of the moral person in the electoral publicity crimes is subject to the general provisions included in the general section of the Penal Code and the Code of Criminal Procedure concerning the general procedural rules applicable to the natural person that are in line with the nature of the moral person, which constitutes a legislative deficiency as its adoption is not sufficient to establish of the penal liability of the moral person in the electoral publicity crimes, without creating an integral procedural system for prosecuting the moral person when he commits the electoral publicity crimes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.978

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 studies0.0000.001
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 head0.036
GPT teacher head0.332
Teacher spread0.296 · 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
Published2019
Admission routes1
Has abstractyes

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