Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays

Abstract

Variation in college application materials related to social stratification 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 US 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 different aspects the transfer application itself correlate with institutional stratification and make students more or less distinguishable? We use a dataset of 20,532 transfer admissions essays submitted to the University of California system to describe how transfer applicants vary linguistically, culturally, and narratively with respect to academic pathways and essay prompts. Using a variety of methods for computational text analysis and qualitative coding, we find that essays written by community college students tend to be distinct from those written by university students. However, the strength and character of these results changed with the writing prompt provided to applicants. These results show how some forms of stratification, such as the type of school students attend, inform educational processes intended to equalize opportunity and how combining computational and human reading might illuminate these patterns. Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-022-00185-5.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.