This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highl...

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Bibliographic Details

Main Author:	Piper, Andrew 1973- (Author)
Format:	Electronic eBook
Language:	English
Published:	Cambridge Cambridge University Press 2020
Subjects:	Criticism, Textual Computerunterstütztes Verfahren Evidenz Maschinelles Lernen Generalisierung Textanalyse
Online Access:	BSB01 UBG01 Volltext
Summary:	This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment
Item Description:	Title from publisher's bibliographic system (viewed on 21 Sep 2020)
Physical Description:	1 Online-Ressource (78 Seiten)
ISBN:	9781108922036
DOI:	10.1017/9781108922036