To answer questions about the relationships between intersectionality, geography, and textual production, we analyze a corpus of essays written by every in-state Latinx identifying applicant (n = 254,820 essays submitted by 83,538 applicants) to the University of California system over two admissions cycles (2015-2017). After computationally modeling the essay content and style of the essays, we then predict different identity characteristics of applicants and spatial characteristics of their school communities. Essay content and style are very strong predictors of nearly all of the different outcomes and data compared and are stronger than previously reported results on similar data. We complement these results with an analysis of applicants that were misclassified in our studies and found that first gen., low income women from areas with high proportions of White residents and lower median income had the highest rates of misclassification.
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