Can We Be Wrong? The Problem of Textual Evidence in a Time of Data (Paperback)

Andrew Piper

ISBN 10: 1108926207 / ISBN 13: 9781108926201

Editore: Cambridge University Press, Cambridge, 2020

Riguardo questo articolo

Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9781108926201

Dati bibliografici

• Titolo: Can We Be Wrong? The Problem of Textual Evidence in a Time of Data (Paperback)
• Editore: Cambridge University Press, Cambridge
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1108926207
• ISBN 13: 9781108926201
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies.

Informazioni sull?autore

Andrew Piper is Professor and William Dawson Scholar in the Department of Languages, Literatures, and Cultures at McGill University. He is the director of .txtLAB, a laboratory for cultural analytics, and editor of the Journal of Cultural Analytics. He is also the author of Enumerations: Data and Literary Study (Chicago 2018).