Neutrosophical statistical analysis of the knowledge of the incidents related to the execution of the sentence and the right to effective judicial protection

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Genaro Vinicio Jordán Naranjo
Mónica Alexandra Salame Ortiz
Dayana Vanessa Naranjo Hilaño

Abstract

The purpose of the execution of the custodial sentence is to guarantee a system that favors the exercise of rights and responsibilities of persons deprived of liberty, as part of a group of priority attention, through the promotion of social reintegration and the progressive system whose objective is focused on the development of their capacities and competencies in order to reintegrate rehabilitated, productive citizens into society with regulatory compliance. This system has three regimes: closed, semi-open and open, to which the person deprived of liberty has the right to access and move from one regime to another as long as he/she complies with the requirements and conditions set forth in the corresponding regulations; the request to access this change of regime must be submitted to the Judge of Penitentiary Guarantees, who will summon the parties to a hearing and verify compliance with the requirements set forth in the regulations. The objective of the present research is to carry out a statistical and neurological analysis for the evaluation of the knowledge of law students about the incidents related to the execution of the sentence and the right to effective judicial protection. A descriptive non-experimental research was used, where the results obtained showed the need for more in-depth studies.

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Neutrosophical statistical analysis of the knowledge of the incidents related to the execution of the sentence and the right to effective judicial protection. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 28, 203-212. http://fs.unm.edu/NCML2/index.php/112/article/view/401
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How to Cite

Neutrosophical statistical analysis of the knowledge of the incidents related to the execution of the sentence and the right to effective judicial protection. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 28, 203-212. http://fs.unm.edu/NCML2/index.php/112/article/view/401