Income and Campus Application Disparities Among European and Non-European Heritage Hispanic Undergraduate Applicants

Alvero, AJ; Giebel, Sonia; Pearman, Francis

doi:10.35542/osf.io/ske5m

Cited by 2 publications

(1 citation statement)

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“…7. The decision to self-classify as Hispanic on college applications is particularly prone to wide-ranging interpretations (Giebel, Alvero, and Pearman 2022;Huang 2023). Peter likewise did not perceive applicants who self-classified as Black as facing the same scrutiny.…”

Section: Discussionmentioning

confidence: 99%

Translating Authentic Selves into Authentic Applications: Private College Consulting and Selective College Admissions

Huang

2023

Sociol Educ

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Stratification in selective college admissions persists even as colleges’ criteria for evaluating merit have multiplied in efforts to increase socioeconomic and racial diversity. Middle-class and affluent families increasingly turn to privatized services, such as private college consulting, to navigate what they perceive to be a complicated and opaque application process. How independent educational consultants (IECs) advise students can thus serve as a lens for understanding how the rules of college admissions are interpreted and taught to students. Through 50 in-depth interviews with IECs, I find that IECs encourage students to be authentic by being true to themselves but that demonstrating authenticity requires attention to how one’s authentic self will be perceived. Translating an authentic self into an authentic application also involves class-based and racialized considerations, particularly for Asian American students who are susceptible to being stereotyped as inauthentic. These findings suggest that efforts to improve diversity must be carefully implemented, or they risk reproducing inequality.

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Section: Discussionmentioning

confidence: 99%

Translating Authentic Selves into Authentic Applications: Private College Consulting and Selective College Admissions

Huang

2023

Sociol Educ

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Sociolinguistic Perspectives on Machine Learning with Text Data

Alvero

2023

The Oxford Handbook of the Sociology of Machine Learning

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Digitized text has become a popular form of data for sociologists. But text is also the product of many different social and linguistic processes, a topic traditionally examined by sociolinguists in the case of spoken language. This chapter presents a sociolinguistic perspective as a methodological framework when using machine learning to analyze text data. The chapter describes sociolinguistic theories and approaches to studying language while also highlighting ways that this literature tends to differ from traditional sociology. To exemplify this approach, the chapter analyzes a corpus of college admissions essays written by Latinx-identifying applicants using two popular machine-learning-based methods: topic modeling and word embeddings. Similar to variation in spoken language among constituent groups and ethnicities within the panethnic category (e.g., Mexican, Cuban), written language also varies by subgroup in meaningful ways. A sociolinguistic perspective for computational text analysis could spur theoretical and empirical insights.

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Income and Campus Application Disparities Among European and Non-European Heritage Hispanic Undergraduate Applicants

Cited by 2 publications

References 4 publications

Translating Authentic Selves into Authentic Applications: Private College Consulting and Selective College Admissions

Translating Authentic Selves into Authentic Applications: Private College Consulting and Selective College Admissions

Sociolinguistic Perspectives on Machine Learning with Text Data

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