Proximal Reading: a network-scaled approach to digital literature analysis
Menadue, Christopher B. (2017) Proximal Reading: a network-scaled approach to digital literature analysis. In: [Presented at the CASE HDR Conference 2017]. From: CASE HDR Conference 2017: Intersecting Fields, 30-31 October 2017, Townsville, QLD, Australia.
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Abstract
Applying quantitative, data-focused approaches to analyse text is not new. Before computers, exigesis, concordance and bibliographic studies covered much of the same ground. Computers speed up processing compared to humans. Digital methodologies have been commonly applied to text since Burrow's landmark work in 1987 on the novels of Jane Austen, and now are commonplace. For many literature analysts they have, however, supplanted some of the rich and detailed examination of specific texts by human scholars, and this is problematic. Proximal reading is designed as an approach that takes elements from both distant and close reading and does so in a package that utilises commonly available software on a scale that makes it achievable for the non-specialist researcher, especially in the field of literature analysis.
Item ID: | 51500 |
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Item Type: | Conference Item (Presentation) |
Keywords: | fiction, literature, methodology, digital humanities, science fiction, computer |
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Date Deposited: | 09 Nov 2017 01:34 |
FoR Codes: | 20 LANGUAGE, COMMUNICATION AND CULTURE > 2002 Cultural Studies > 200299 Cultural Studies not elsewhere classified @ 40% 20 LANGUAGE, COMMUNICATION AND CULTURE > 2005 Literary Studies > 200599 Literary Studies not elsewhere classified @ 40% 20 LANGUAGE, COMMUNICATION AND CULTURE > 2005 Literary Studies > 200508 Other Literatures in English @ 20% |
SEO Codes: | 95 CULTURAL UNDERSTANDING > 9599 Other Cultural Understanding > 959999 Cultural Understanding not elsewhere classified @ 20% 97 EXPANDING KNOWLEDGE > 970119 Expanding Knowledge through Studies of the Creative Arts and Writing @ 50% 95 CULTURAL UNDERSTANDING > 9502 Communication > 950203 Languages and Literature @ 30% |
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