Book contents
- The Dangerous Art of Text Mining
- The Dangerous Art of Text Mining
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Introduction
- Part I Toward a Smarter Data Science
- Chapter 1 Why Textual Data from the Past Is Dangerous
- Chapter 2 From Fantasy to Engagement
- Chapter 3 Words Are Keys and Words Are Barriers
- Chapter 4 Critical Search: A Theory
- Chapter 5 To Predict or to Describe?
- Part II The Hidden Dimensions of Temporal Experience
- Part III Disciplinary Implications
- Appendix: Notes on Data, Code, Labor, Room for Error, and British History
- Index
Chapter 5 - To Predict or to Describe?
from Part I - Toward a Smarter Data Science
Published online by Cambridge University Press: 21 September 2023
- The Dangerous Art of Text Mining
- The Dangerous Art of Text Mining
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgments
- Introduction
- Part I Toward a Smarter Data Science
- Chapter 1 Why Textual Data from the Past Is Dangerous
- Chapter 2 From Fantasy to Engagement
- Chapter 3 Words Are Keys and Words Are Barriers
- Chapter 4 Critical Search: A Theory
- Chapter 5 To Predict or to Describe?
- Part II The Hidden Dimensions of Temporal Experience
- Part III Disciplinary Implications
- Appendix: Notes on Data, Code, Labor, Room for Error, and British History
- Index
Summary
This chapter revisits the fantastical conceit that we can make significant predictions about a society. Engaging recent critiques of predictive logic by Jill Lepore, it touches upon the misuses of partially accurate predictive machines – the marketing research technology that has become part of the fabric of political and social life in our time – often with disastrous consequences. The chapter revisits the fantastic project of Peter Turchin, to map out predictive laws of human society, analyzing him against objections against modeling the evolution of human interactions as law-like, from Karl Popper to William Sewell. It engages the theoretical work of Reinhart Koselleck, who argues that prediction or prophecy is untenable, and that we only have prognosis. It looks at the conditions under which Koselleck imagines “prognosis” to be tenable, including the example of grievance theory, which refrains from predicting how or when a group might respond to historical oppression, and only describes the dynamics and acknowledges when there are “grounds” for revolt. The chapter makes room for readers who have turned to this book out of an interest in modeling or prediction to enter a more robust engagement with history, which is presented here as the source of a rigorous engagement with problems of change over time.
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- The Dangerous Art of Text MiningA Methodology for Digital History, pp. 135 - 158Publisher: Cambridge University PressPrint publication year: 2023