Racial Change in Suburbia: America's Diversifying Elementary Districts and Their Effect on School Segregation.

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Title: Racial Change in Suburbia: America's Diversifying Elementary Districts and Their Effect on School Segregation.
Authors: Mann, Bryan1 (AUTHOR) bryanmann@ku.edu, Fitzpatrick, Ryan1 (AUTHOR) ryanfitz@ku.edu, Hammouda, Daniah1 (AUTHOR) d738h775@ku.edu
Source: AERA Open. 11/07/2024, Vol. 10, p1-19. 19p.
Abstract: The ethnic and racial makeup of the United States has changed during the last several decades. Scholars have qualitatively shown how these changes affect school districts but have not identified their scale. We examine residential demographic change using a novel dataset derived from a geographic technique that leverages satellite imagery with 2000 and 2020 census data. We then analyze the effects of demographic change on student isolation and dissimilarity in elementary school districts. Our findings indicate that most elementary districts have changed, but the changes are uneven. The number of districts with White student intense isolation declined, particularly in suburban locales. Meanwhile, districts with student of color intense isolation increased, particularly in urban locales. These changes indicate that increasing diversity for White students has coincided with greater isolation for students of color. These findings should prompt scholars to reconsider their conceptualization of suburban school districts. [ABSTRACT FROM AUTHOR]
Copyright of AERA Open is the property of Sage Publications Inc. 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: Racial Change in Suburbia: America's Diversifying Elementary Districts and Their Effect on School Segregation.
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  Data: <searchLink fieldCode="AR" term="%22Mann%2C+Bryan%22">Mann, Bryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bryanmann@ku.edu</i><br /><searchLink fieldCode="AR" term="%22Fitzpatrick%2C+Ryan%22">Fitzpatrick, Ryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ryanfitz@ku.edu</i><br /><searchLink fieldCode="AR" term="%22Hammouda%2C+Daniah%22">Hammouda, Daniah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d738h775@ku.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22AERA+Open%22">AERA Open</searchLink>. 11/07/2024, Vol. 10, p1-19. 19p.
– Name: Abstract
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  Data: The ethnic and racial makeup of the United States has changed during the last several decades. Scholars have qualitatively shown how these changes affect school districts but have not identified their scale. We examine residential demographic change using a novel dataset derived from a geographic technique that leverages satellite imagery with 2000 and 2020 census data. We then analyze the effects of demographic change on student isolation and dissimilarity in elementary school districts. Our findings indicate that most elementary districts have changed, but the changes are uneven. The number of districts with White student intense isolation declined, particularly in suburban locales. Meanwhile, districts with student of color intense isolation increased, particularly in urban locales. These changes indicate that increasing diversity for White students has coincided with greater isolation for students of color. These findings should prompt scholars to reconsider their conceptualization of suburban school districts. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of AERA Open is the property of Sage Publications Inc. 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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        Value: 10.1177/23328584241290754
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