Linguistic, Cultural, and Narrative Capital: Computational and Human Readings of Transfer Admissions Essays

Abstract

Stratification in college application materials is a contentious topic in social science and national discourse in the United States. This line of research has also started to use computational methods to consider qualitative materials, such as personal statements and letters of recommendation. Despite the prominence of this topic, fewer studies have considered a fairly common academic pathway: transferring. Approximately 40\% of all college students in the United States transfer schools at least once. One quirk of the system is that students from community colleges are applying for the same spots for students already enrolled in four year schools and trying to transfer. How might these different institutional experiences and the transfer application itself reflect social stratification? We leverage a dataset of 20,532 transfer admissions essays submitted to the University of California system to describe how transfer applicants are stratified linguistically, culturally, and narratively with respect to academic pathways and essay prompts. These results show different types of stratification that can emerge in educational processes intended to equalize opportunity and how combining computational and human reading might illuminate them.

Authorship Identity and Spatiality: Social Influences on Text Production

“…Though this might be typically done with something like a density plot or correlation matrix, here we follow (Alvero et al, 2022) and use t-tests to compare average values by group. Specifically, we use Welch's t-test to account for any differences in group size.…”

“…were not completely surprising. (Alvero et al, 2020;Alvero et al, 2022) and its relative interpretability compared to more computationally intensive methods (Hastie, Tibshirani, and Friedman 2009). The results are presented in two ways and in the following order: repeated undersampling with ten fold cross validation and the IMV.…”

“…[something about the intersectionality dictionary if we include it]. We follow (Alvero et al, 2021;Alvero et al 2022) in our computational approach to modeling the essays, including textual preprocessing. Specifically, we use correlated topic modeling (CTM; Blei & Laffert, 2006) and the linguistic inquiry and word count (LIWC; Pennebaker et al, 2015) software package to generate variables that capture the content and writing style of the essays.…”

“…Previous work had found that admissions essays are strong predictors of author demographics in the supervised setting, such as predicting whether an applicant is above or below the median income and/or their gender (Alvero et al 2020). Another study of transfer admissions essays similarly found that essays are strongly predictive of whether or not applicants are enrolled in community colleges or four year schools (Alvero et al 2022). However, none of these studies took an intersectional approach to consider how the experiences of low income women, for example, might likewise be encoded into the essays with a high degree of accuracy.…”

“…life sciences). Sociologists in particular have been analytically flexible, sometimes pairing machine learning results with qualitative reading (Nelson et al 2021;Nelson;Alvero et al 2022] for deeper analysis than what one methodological toolkit alone could provide. Another likely reason is availability.…”