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Acknowledgments
Introduction. Tools, Tests, and Data: An Introduction to the New History and Philosophy of Science
Part I. Toward a New Logic of Scientific Discovery, Creativity, and Progress
1. Five Models of Science, Illustrating How Selection Shapes Methods
2. Pooling with the Best
3. Promoting Diverse Collaborations
4. Using Phylomemies to Investigate the Dynamics of Science
Part II. Frontiers in Tools, Methods, and Models
5. LDA Topic Modeling: Contexts for the History and Philosophy of Science
6. The petential of Supervised Machine Learning for the study of Science
7. Help with Data Management for the Novice and Experienced Alike
8. How Not to Fight about Theory: The Debate between Biometry an Mendelism in Nature, 1890-1915
9. Topic Modeling in HPS: Investigating Engaged Philosophy of Science throughout the Twentieth Centry
10. Bolzano, Kant, and the Traditional Theory of Concepts: A Computational Investigation
11. The Evolution of Evolutionary Medicine
Notes
Reference
Index

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The dynamics of science : computational frontiers in history and philosophy of science 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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Millions of scientific articles are published each year, making it difficult to stay abreast of advances within even the smallest subdisciplines. Traditional approaches to the study of science, such as the history and philosophy of science, involve closely reading a relatively small set of journal articles. And yet many questions benefit from casting a wider net: Is most scientific change gradual or revolutionary? What are the key sources of scientific novelty? Over the past several decades, a massive effort to digitize the academic literature and equip computers with algorithms that can distantly read and analyze a digital database has taken us one step closer to answering these questions. The Dynamics of Science brings together a diverse array of contributors to examine the largely unexplored computational frontiers of history and philosophy of science. Together, they reveal how tools and data from automated textual analysis, or machine "reading," combined with methods and models from game theory and cultural evolutionary theory, can begin to answer fundamental questions about the nature and history of science.

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