Andrew Piper (Montreal McGill University)

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

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Elements in Digital Literary Studies
• Verlag: Cambridge University Press
• Seitenzahl: 75
• Erscheinungstermin: 19. November 2020
• Englisch
• Abmessung: 229mm x 153mm x 10mm
• Gewicht: 142g
• ISBN-13: 9781108926201
• ISBN-10: 1108926207
• Artikelnr.: 60019506

Autorenporträt

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).

Inhaltsangabe

Introduction, or What's Wrong with Literary Studies?
Part I. Theory: 1. Probable Cause
Part II. Evidence Eve Kraicer, Nicholas King, Emma Ebowe, Matthew Hunter, Victoria Svaikovsky, and Sunyam Bagga
2. Machine Learning as a Collaborative Process
3. Results
Part III. Discussion: 4. Don't Generalize (from Case Studies): The Case for Open Generalization
5. Don't Generalize (At All): The Case for the Open Mind
Conclusion: On the Mutuality of Method.