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

Author

Listed:
  • AJ Alvero

    (University of Florida)

  • Jasmine Pal

    (University of California, Los Angeles)

  • Katelyn M. Moussavian

    (University of California, Los Angeles)

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.

Suggested Citation

  • AJ Alvero & Jasmine Pal & Katelyn M. Moussavian, 2022. "Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays," Journal of Computational Social Science, Springer, vol. 5(2), pages 1709-1734, November.
  • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00185-5
    DOI: 10.1007/s42001-022-00185-5
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    References listed on IDEAS

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    1. Laura K. Nelson & Derek Burk & Marcel Knudsen & Leslie McCall, 2021. "The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods," Sociological Methods & Research, , vol. 50(1), pages 202-237, February.
    2. Dustin S. Stoltz & Marshall A. Taylor, 2019. "Concept Mover’s Distance: measuring concept engagement via word embeddings in texts," Journal of Computational Social Science, Springer, vol. 2(2), pages 293-313, July.
    3. Rothstein, Jesse, 2022. "Qualitative information in undergraduate admissions: A pilot study of letters of recommendation," Economics of Education Review, Elsevier, vol. 89(C).
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. Terry T. Ishitani & Lee D. Flood, 2018. "Student Transfer-Out Behavior at Four-Year Institutions," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(7), pages 825-846, November.
    6. Laura K. Nelson, 2020. "Computational Grounded Theory: A Methodological Framework," Sociological Methods & Research, , vol. 49(1), pages 3-42, February.
    7. Jae Yeon Kim, 2021. "Integrating human and machine coding to measure political issues in ethnic newspaper articles," Journal of Computational Social Science, Springer, vol. 4(2), pages 585-612, November.
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    Cited by:

    1. Alvero, AJ & Luqueño, Leslie & Pearman, Francis & antonio, anthony lising, 2022. "Social Influences on Textual Production: Intersectionality, Geography, and College Admissions Essays," SocArXiv pt6b2, Center for Open Science.

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