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  5. DIGITAL INCLUSION IN THE LIS LITERATURE: AN INTERSECTIONAL ANALYSIS
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DIGITAL INCLUSION IN THE LIS LITERATURE: AN INTERSECTIONAL ANALYSIS

Date Issued
May 1, 2023
Author(s)
Fountain, Hannah Nichole  
Advisor(s)
Brian J. Dobreski
Additional Advisor(s)
Peilin Wang
Vandana Singh
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/45804
Abstract

Digital inclusion refers to the conditions and degrees of access to information and communication technologies (ICT) among individuals and communities. This includes the variable determinants and outcomes associated with ICT connectivity, as well as efforts to mitigate digital exclusion. With the proliferation of ICT in the past 30 years, digital inclusion (and related concepts like the digital divide and digital literacy) has been a major focus of policymaking and public service efforts, with libraries serving as leaders in offering free public ICT and digital skills training. Digital inclusion research has commonly relied upon sociodemographic variables to survey determinants of digital inequality, with digital inequalities often characterized as reproductions and expansions of extant structural and social inequalities. The overlap and mutuality of digital inequalities is a consistent theme in digital inclusion studies, echoing the major points of intersectional theory, which seeks to understand inequality and discrimination as a complex multiaxial experience. In performing content analysis across the library-focused library and information science (LIS) literature, this thesis plans to use an intersectional framework to observe the relationship between presently surveyed digital in/exclusion and structural inequalities, and to provide an account of the myriad ways libraries engage with digital inclusion.

Subjects

digital inclusion

intersectionality

library and informati...

digital divide

Disciplines
Library and Information Science
Social Justice
Degree
Master of Science
Major
Information Sciences
File(s)
Thumbnail Image
Name

H.Fountain_Thesis_Final_4.27.2023.docx

Size

1.95 MB

Format

Microsoft Word XML

Checksum (MD5)

eb78ed9610269f8cf4cae16495b652e6

Thumbnail Image
Name

auto_convert.pdf

Size

1.53 MB

Format

Adobe PDF

Checksum (MD5)

ecb7bc812e0dac9a00535a57f6f031ef


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