Neutrosophic inference method for the analysis of indigenous benefits in Ecuador and their impact on social and economic justice

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Wendy Natalia Toapanta Gamboa
Carlos Alberto García Valle
Juan José López García
Iruma Alfonso González

Abstract

The discussion about the benefits granted to indigenous communities in Ecuador has given rise to divergent opinions regarding their influence on social and economic equity. Although some view them as an instrument of justice and historical reparation, others argue that they can cause structural inequalities in access to resources and opportunities for other segments of the community. Based on the aforementioned problems, this research aims to develop a method for analyzing indigenous benefits in Ecuador and their impact on social and economic justice. The result was the use of 2-tuple neutrosophic number sets, which are based on an evaluation using a linguistic scale within the context of Word Computing. The use of neutrosophic sets allows for the incorporation of uncertainty derived from a lack of information or from contradictory and inconsistent data, situations that often arise in everyday problems. The findings indicate that, although the benefits allocated to indigenous groups attempt to rectify historical inequalities, their implementation must take into account criteria of proportionality and distributive equity to prevent further imbalances in society. They highlight the importance of establishing fair public policies that enable historical reparations to coexist with equitable access to opportunities for all social strata.

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Neutrosophic inference method for the analysis of indigenous benefits in Ecuador and their impact on social and economic justice. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 38, 389-399. https://fs.unm.edu/NCML2/index.php/112/article/view/820
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How to Cite

Neutrosophic inference method for the analysis of indigenous benefits in Ecuador and their impact on social and economic justice. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 38, 389-399. https://fs.unm.edu/NCML2/index.php/112/article/view/820

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