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Cover: 9781108926201 | Can We Be Wrong? the Problem of Textual Evidence in a Time of Data
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Can We Be Wrong? The Problem of Textual Evidence in a Time of Data
Taschenbuch von Andrew Piper
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
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).
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2020
Fachbereich: Programmiersprachen
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108926201
ISBN-10: 1108926207
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Piper, Andrew
Hersteller: Cambridge University Press
Maße: 229 x 153 x 10 mm
Von/Mit: Andrew Piper
Erscheinungsdatum: 19.11.2020
Gewicht: 0,136 kg
Artikel-ID: 118871559
Über den Autor
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).
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2020
Fachbereich: Programmiersprachen
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108926201
ISBN-10: 1108926207
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Piper, Andrew
Hersteller: Cambridge University Press
Maße: 229 x 153 x 10 mm
Von/Mit: Andrew Piper
Erscheinungsdatum: 19.11.2020
Gewicht: 0,136 kg
Artikel-ID: 118871559
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