Gender identity and access to higher education.
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| Title: | Gender identity and access to higher education. |
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| Authors: | Maupin, I. (AUTHOR), McCannon, Bryan C. (AUTHOR) |
| Source: | Studies in Higher Education. Sep2025, Vol. 50 Issue 9, p1848-1870. 23p. |
| Subjects: | Gender identity, Higher education, University & college admission, Content analysis, Pronouns, Discrimination (Sociology), Machine learning |
| Geographic Terms: | United States |
| Abstract: | Our research examines the potential gender identity discrimination within higher education in the U.S. An audit study was conducted by sending emails to admission counselors, where the messages varied in the inclusion of gender pronouns in the signature line. The results indicate a higher response rate for emails which included preferred pronouns, with a response rate increase of approximately four percentage points, regardless of the type of pronoun used. We engage in text analysis and show that responses to inquiries with pronouns received more friendly responses receiving heightened use of exclamation marks, emojis/emoticons, and from a topic modeling algorithm were less likely to be strictly replies explaining the admission process. Finally, we apply machine learning to identify key institution attributes that are useful in predicting heterogeneous responses, and to identify the attributes of institutions where negative discrimination is likely to occur. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Our research examines the potential gender identity discrimination within higher education in the U.S. An audit study was conducted by sending emails to admission counselors, where the messages varied in the inclusion of gender pronouns in the signature line. The results indicate a higher response rate for emails which included preferred pronouns, with a response rate increase of approximately four percentage points, regardless of the type of pronoun used. We engage in text analysis and show that responses to inquiries with pronouns received more friendly responses receiving heightened use of exclamation marks, emojis/emoticons, and from a topic modeling algorithm were less likely to be strictly replies explaining the admission process. Finally, we apply machine learning to identify key institution attributes that are useful in predicting heterogeneous responses, and to identify the attributes of institutions where negative discrimination is likely to occur. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 03075079 |
| DOI: | 10.1080/03075079.2024.2403730 |
