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Inequities in Public Scholarship during the Pandemic: Who Made Predictions about the Future of Higher Education?

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Title: Inequities in Public Scholarship during the Pandemic: Who Made Predictions about the Future of Higher Education?
Authors: Long, Kyle1, Streitwieser, Bernhard2, Gitter, Joy3
Source: Current Issues in Comparative Education. Winter2024, Vol. 26 Issue 1, p3-28. 26p.
Subject Terms: *Scholarly method, *COVID-19 pandemic, *Teaching methods, Trade publications, White men
Abstract: Throughout the Covid-19 pandemic, commentators in broadly accessible media have offered a surfeit of predictions about the future of higher education. Due to the absence of accountability mechanisms, however, the accuracy of these claims has been heretofore unknown. Research shows that op-eds and other forms of public scholarship influence public policy, heightening the significance of predictions. This paper asks who makes predictions about higher education, in what venues they issue them, on what topics they make predictions, and how accurate they are. It answers these questions by drawing from an original data set of 91 distinct predictions issued by 22 unique authors in 31 separate texts across a 19-month time span from March 2020 to October 2021. It finds that predictions most often appeared in op-eds written by senior academic white men in higher education trade journals. More than half of predictions could not be evaluated a year or more after they were first issued. Still, predictions with determinable outcomes tended to bear out accurately. Enrollment patterns and teaching modalities were the most common topics. Women and people of color were significantly under-represented among predictors. The paper concludes with suggestions for improving equity and performance. [ABSTRACT FROM AUTHOR]
Copyright of Current Issues in Comparative Education is the property of Current Issues in Comparative Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Throughout the Covid-19 pandemic, commentators in broadly accessible media have offered a surfeit of predictions about the future of higher education. Due to the absence of accountability mechanisms, however, the accuracy of these claims has been heretofore unknown. Research shows that op-eds and other forms of public scholarship influence public policy, heightening the significance of predictions. This paper asks who makes predictions about higher education, in what venues they issue them, on what topics they make predictions, and how accurate they are. It answers these questions by drawing from an original data set of 91 distinct predictions issued by 22 unique authors in 31 separate texts across a 19-month time span from March 2020 to October 2021. It finds that predictions most often appeared in op-eds written by senior academic white men in higher education trade journals. More than half of predictions could not be evaluated a year or more after they were first issued. Still, predictions with determinable outcomes tended to bear out accurately. Enrollment patterns and teaching modalities were the most common topics. Women and people of color were significantly under-represented among predictors. The paper concludes with suggestions for improving equity and performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Current Issues in Comparative Education is the property of Current Issues in Comparative Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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