Eric Garcia

This article argues that (C) the term “Afro-Latinx” is more apt than “Hispanic” or “Latinx” in a significant number of cases. Three premises support this conclusion. The first premise (P1) is that use of “Afro-Latinx” provides subjects with understanding of how certain events depend on anti-Black racism, US society’s racially unjust structure, and US colonial policy. The second premise (P2) is that neither the term “Hispanic” nor the term “Latinx” provides subjects with this understanding of how certain events depend on anti-Black racism, US society’s racially unjust structure, and US colonial policy. The third premise (P3) is that the term “Afro-Latinx” provides subjects with more understanding of these events than the terms “Hispanic” and “Latinx.” To motivate the argument, the article presents the case of Jose “Kiko” García’s murder by the New York City police and the uprising that ensued.

I will argue that just and accurate assessment must involve taking into account how racial injustice affects students’ performance in their work. To this end, I will motivate what I call the RACIAL-INJUSTICE-ASSESSMENT THESIS. According to this thesis, instructors must account for how racial injustice affects a student’s work for an instructor’s judgment of her work to count as just. To motivate the RACIAL-INJUSTICE ASSESSMENT THESIS, I will defend the ACCURACY THESIS and the JUSTICE THESIS. According to the ACCURACY THESIS, the accuracy of an instructor’s judgment of a student’s work will covary with the degree to which she considers how racial injustice affects the performance enshrined in the student’s the work. According to the JUSTICE THESIS, the justness of instructors’ judgments of student work covary with the degree to which instructors consider racial injustice’s effect on student work.

This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem, and the second is based on a new mathematical model. By solving VSP, we can find nodes that cause the rupture of the giant component. On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months. This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior.