Neutrosophic multicriteria method to assess the impact of the repeal of the table of admissible quantities on the criminalization of the consumer

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Geovanny Leopoldo Borja Martínez
Jhossthin Manolo Llerena Llerena
Ronald Alexander Alomaliza Garcés
Frida Alejandra Villamarín Chumaña

Abstract

On November 24, 2024, Presidential Decree No. 28 established the elimination of the list of drugs, which has sparked intense debate in the legal and academic spheres of Ecuador. This research aims to implement a neutrosophic multicriteria method to evaluate the impact of the repeal of the table of admissible quantities on the criminalization of users. The effects of this repeal on criminal theory are analyzed, focusing on how it has altered the legal reference range regarding the quantities allowed for the consumption of controlled substances. The findings indicate that the elimination of a regulatory system that differentiated between drug use and trafficking has led to an increase in criminal charges against drug users. This is especially problematic given that the Ecuadorian Constitution prohibits the criminalization of users, considering it a public health issue. Research suggests that the repeal of the table of permitted quantities not only creates a legal loophole in the criminal justice system but also compromises public health by criminalizing consumption rather than addressing the problem from a health and prevention perspective. This neutrosophic approach will allow a better understanding of the multiple dimensions of the impact of this repeal and contribute to the discussion of more effective policies.

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Neutrosophic multicriteria method to assess the impact of the repeal of the table of admissible quantities on the criminalization of the consumer. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 38, 301-310. https://fs.unm.edu/NCML2/index.php/112/article/view/811
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How to Cite

Neutrosophic multicriteria method to assess the impact of the repeal of the table of admissible quantities on the criminalization of the consumer. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 38, 301-310. https://fs.unm.edu/NCML2/index.php/112/article/view/811

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