Jorge Oseguera Gamba

Este estudio examina la detección de noticias falsas entre adolescentes utilizando técnicas de Machine Learning. Se plantean dos preguntas fundamentales: cómo reaccionan los jóvenes ante la desinformación y qué nivel de confianza tienen en la veracidad de las noticias. A través de un análisis detallado con algoritmos de aprendizaje automático, como k-means, Decision Trees y Random Forest, se clasificaron las respuestas de los participantes, revelando patrones significativos en su confianza hacia los medios de comunicación. Los hallazgos sobre esta muestra indican que aproximadamente el 15% de los adolescentes no confían plenamente en las noticias y casi el 50% carece de habilidades para identificar información falsa. Esto resalta la necesidad urgente de implementar programas de alfabetización mediática que fortalezcan la capacidad crítica de los jóvenes para discernir la veracidad de la información en un entorno digital saturado de desinformación. Además, el estudio sugiere que futuras investigaciones deben explorar otros algoritmos de Machine Learning y evaluar el impacto de intervenciones educativas en la alfabetización mediática.

In Mexico, women entrepreneurs represent a significant portion of the population that contributes economically to the country. Few studies describe the challenges they face in creating and maintaining businesses, such as access to resources and the motivations that influence their intention and commitment to entrepreneurship, given that it is considered a predominantly male activity. To describe this behavioral intention that precedes entrepreneurship, the Theory of Reasoned Action (TRA) is used as a theoretical framework. However, unlike previous applications of TRA in entrepreneurship research—which predominantly treat intention as a static predictor of the decision to start a business—we broaden the framework by conceptualizing behavioral intention as a continuous motivational force that sustains entrepreneurial passion and deepens organizational commitment in the entrepreneurial process. In doing so, the study addresses a gap in the literature: TRA has been criticized for neglecting the psychological mechanisms that underpin entrepreneurial behavior once the business has been launched. Based on in-depth interviews with ten women entrepreneurs in Cuernavaca, Morelos (Mexico), the findings show that entrepreneurial intention does not dissolve once the business is created: it re-emerges as entrepreneurial passion during the uncertain early stages of the venture and, as the business incorporates employees and customers, crystallizes into organizational commitment, which in turn feeds back into and reinforces entrepreneurial passion. These findings are synthesized into an integrative, sequential model—intention, behavior, passion, and commitment—that offers a novel processual account of women’s entrepreneurship beyond the moment of business creation.

Entrevista realizada a la doctora Natalia Quiceno Toro, profesora investigadora del Instituto de Estudios Regionales (INER) de la Universidad de Antioquia.

Existe poca controversia cuando decimos que buscamos que nuestras vidas tengan bienestar, o que el bienestar es deseable. La mayoría de nosotros tenemos un conjunto de referencias acerca de lo que significa e implica que una vida tenga o no tenga bienestar. También, se trata de un concepto que escuchamos con frecuencia en discursos de políticas públicas y análisis de poblaciones, en donde se justifican programas o intervenciones aludiendo a que fomentan el bienestar de los involucrados, o se comparan los niveles de bienestar en distintos lugares del mundo. En este sentido, resulta evidente que se trata de un término fundamental. Sin embargo, de que sea fundamental no se sigue que tengamos claro cuál es su significado, características o implicaciones para nuestras vidas individuales y colectivas. La filosofía puede ayudarnos a ordenar y refinar nuestras ideas relacionadas con el bienestar, haciendo explícitos nuestros argumentos y permitiéndonos analizarlos a profundidad. También nos permite explorar las posibles implicaciones de los conceptos que ofrecemos.

This study presents an interdisciplinary methodology for the Resource Allocation Problem (RAP) in a textile manufacturing environment, aligned with the principles of Industry 5.0, which emphasizes the convergence of advanced technologies and the human factor. The core of this research lies in the integration of an Artificial Neural Network (ANN) to predict worker performance, coupled with process automation, to optimize the allocation of workers to machines. A key ethical consideration addressed by this methodology is the focus on psychosocial factors and workplace well-being, recognizing their contribution to sustainability and organizational success. The benefits of this interdisciplinary approach to RAP are manifold. By considering psychosocial and productivity factors in the optimization model, the study moves beyond traditional methods that often prioritize efficiency at the expense of human considerations. This human-centered approach leads to a more sustainable and socially responsible production environment. The methodology contributes to the development of new tools for managing production in ways that are not only efficient but also significantly aligned with worker well-being. By embedding human-centered constraints directly into the optimization process, this study offers a pathway to mitigate potential issues such as worker overload and improve overall worker satisfaction. Ultimately, this research underscores the value of interdisciplinarity in operational research and production management by demonstrating a cohesive methodology that balances efficiency with ethical considerations for the workforce.

In this article, we critically examine the implementation of what Carlos Montemayor calls Humanitarian Artificial Intelligence from a decolonizing perspective. We highlight the dichotomy of optimism and fear surrounding AI, elucidating its potential to address fundamental human problems and the risks of monopolistic control. We critique Montemayor’s proposal to align AI with a human rights framework, arguing that it insufficiently addresses global inequalities. Our tripartite analysis focuses on the distribution of AI resources, language inclusion, and content diversity to ensure AI benefits all humanity. We emphasize the need for equitable access to AI, linguistic diversity in AI training data, and the preservation of marginalized epistemologies. We advocate for strategies to mitigate environmental impacts and avoid cultural imperialism disguised as altruism, calling for a balanced approach between private sector innovation and state regulation to foster a truly humanitarian AI.

In this article, we advance a realist response to Sharon Street’s debunking argument, which appeals to evolutionary theory to argue that natural selection and moral realism are incompatible. Our aims are three. First, we describe the debate on this topic by distinguishing between a modal argument, a parsimony argument, and Street’s Darwinian Dilemma. Secondly, we focus on her reasoning, one of the most relevant arguments in the recent metaethical debate against moral realism. To mount a defense of that, we will point at three fundamental characteristics of moral realism: its cognitive character, the nature of its representative language, and the relationship between evaluative judgements and their truthmakers (facts). Finally, we will argue, contra Street, that moral realism is not scientifically inferior to moral antirealism, thus, the former is not debunked.