How diverse is your reading list? Exploring issues of representation and decolonisation in the UK.

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Title: How diverse is your reading list? Exploring issues of representation and decolonisation in the UK.
Authors: Schucan Bird, K.1 k.bird@ucl.ac.uk, Pitman, Lesley2
Source: Higher Education (00181560). May2020, Vol. 79 Issue 5, p903-920. 18p. 2 Charts.
Subject Terms: *Curriculum planning, *Book selection, *Authorship, *Educational change, *Curriculum, *Education research, *Higher education, Decolonization
Geographic Terms: United Kingdom
Abstract: There is a growing impetus, from university students and administrations, to decolonise the curriculum and develop diverse reading lists. Yet, there is limited theoretical or empirical analysis of the authorship of current reading lists to justify this imperative. The present study developed and applied a method for auditing the authorship on reading lists of two modules, one from science and one from social science, in a research-intensive British university. The paper explores whether these reading lists can be considered descriptively representative of the student body or scholarly community. The analysis of reading lists found empirical basis for concerns that university curricula are dominated by white, male and Eurocentric authors, with some exceptions. The reading lists did not represent the diverse local student body but came closer to representing the demographic profile of academic staff. To interpret these findings, the paper argues that reading lists have a role to play in decolonising our universities, and offer opportunities to critique and deconstruct disciplinary boundaries. Further research is required to explore student and staff views of reading lists and the meaning of diversity, to evaluate existing tools, and address barriers to decolonising our curricula on a global scale. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education (00181560) is the property of Springer Nature 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.)
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  Data: There is a growing impetus, from university students and administrations, to decolonise the curriculum and develop diverse reading lists. Yet, there is limited theoretical or empirical analysis of the authorship of current reading lists to justify this imperative. The present study developed and applied a method for auditing the authorship on reading lists of two modules, one from science and one from social science, in a research-intensive British university. The paper explores whether these reading lists can be considered descriptively representative of the student body or scholarly community. The analysis of reading lists found empirical basis for concerns that university curricula are dominated by white, male and Eurocentric authors, with some exceptions. The reading lists did not represent the diverse local student body but came closer to representing the demographic profile of academic staff. To interpret these findings, the paper argues that reading lists have a role to play in decolonising our universities, and offer opportunities to critique and deconstruct disciplinary boundaries. Further research is required to explore student and staff views of reading lists and the meaning of diversity, to evaluate existing tools, and address barriers to decolonising our curricula on a global scale. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education (00181560) is the property of Springer Nature 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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