Narrow Results

Drawing upon the historical use of Las Castas — paintings of racialized identity categories during the Spanish colonial era — we use this chapter to explore stories of discrimination and how it persists in Mexico. Borrowing from non-corporeal actant theory, we set out to explore differences and inequality formations of multiple identities. Inequity is symbolized through the names given to biracial and multiracial castes and overtly presented through the arrangement and numbering of categories in the paintings. We overlay contemporary understandings of oppression and opportunity found in the Latin American Public Opinion Project (LAPOP). New antenarratives of hierarchies appear along with the persistence of the old. In particular, the hierarchical positions of those whose ancestors are Spanish and Indigenous have improved over time, while those who identify as Indigenous remain disadvantaged. The stories that we surface suggest that racial differences co-existed in colonial-Mexico and remnants of the caste system may still haunt social life of citizens today. The research attempts to build on scholarship that identified ideas of discrimination as persistent actors in evolving networks. It also contributes to our understanding of how oppressive systems and discrimination are transmedial, persisting as stories embedded in, and reinforced by, complex understandings of interaction. One can plausibly conclude that oral narratives are transmediated both in the art of classic paintings and in response to questions of the LAPOP.

The incompleteness of race and ethnicity information in real-world data (RWD) hampers its utility in promoting healthcare equity. This study introduces two methods—one heuristic and the other machine learning-based—to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthcare.