There is substantial evidence of the relationship between household income and achievement on the standardized tests often required for college admissions, yet little comparable inquiry considers the essays typically required of applicants to selective U.S. colleges and universities. We used a corpus of 240,000 admission essays submitted by 60,000 applicants to the University of California in November 2016 to measure relationships between the content of admission essays, self-reported household income, and SAT scores. We quantified essay content using correlated topic modeling and essay style using Linguistic Inquiry and Word Count. We found that essay content and style had stronger correlations to self-reported household income than did SAT scores and that essays explained much of the variance in SAT scores. This analysis shows that essays encode similar information as the SAT and suggests that college admission protocols should attend to how social class is encoded in non-numerical components of applications.

US higher education has enjoyed growing attention from social scientists and historians. We integrate recent scholarship by framing a political and historical sociology of the sector and we show how higher education has been central to projects of nation building and social provision throughout the course of American political development. US higher education has three institutional configurations: an associational one, defined by voluntary intramural organizations; a national service one, defined by massive government patronage; and a market one, defined by competition for students, patrons, and prestige. Continuity and change over time may be understood with the theoretical tools of historical sociology: path dependence, coalescence, and robust action. Our review substantiates assertions of deep turbulence in US higher education at present and calls for a closer integration of scholarship on state building and social stratification to inform the future.6.1

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How does gender inform initial academic commitments and narrative self-presentation in science, technology, engineering, and mathematics (STEM) fields during the college application process? Analyzing 60,000 undergraduate applications to the University of California, the authors surface two key findings. First, extant gender segregation of academic disciplines also manifests in intended major choice. Additionally, gender and SAT Math scores together strongly predict intent to major in biology and engineering, the most popular and gender-segregated majors. Second, using natural language processing, the investigators find that author gender is more predictive of essay topics written by prospective engineers than prospective biologists. Specifically, women intending to major in engineering write about essay topics that signal their gender identity to a greater degree than women intending to major in biology, perhaps to mitigate gender-transgressive academic commitments. The authors subsequently argue that prescriptive and proscriptive ideas about men and women’s academic choices remain highly salient in a moment of imagining future academic and professional selves.

College admissions in the United States is carried out by a humancentered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings. CCS CONCEPTS • Computing methodologies → Classification and regression trees; • Applied computing → Education; Sociology.