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.
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.
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.
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