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Author: Wolfgang Pietsch
Publisher: Springer Nature
ISBN: 3030864421
Category : Philosophy
Languages : en
Pages : 308
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Book Description
This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science.
Author: Wolfgang Pietsch
Publisher: Springer Nature
ISBN: 3030864421
Category : Philosophy
Languages : en
Pages : 308
Get Book Here
Book Description
This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science.
Author: N. Carlo Lauro
Publisher: Springer
ISBN: 3319554778
Category : Social Science
Languages : en
Pages : 292
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Book Description
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
Author: Shahid Rahman
Publisher: Springer Science & Business Media
ISBN: 1402028083
Category : Philosophy
Languages : en
Pages : 618
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Book Description
The first volume in this new series explores, through extensive co-operation, new ways of achieving the integration of science in all its diversity. The book offers essays from important and influential philosophers in contemporary philosophy, discussing a range of topics from philosophy of science to epistemology, philosophy of logic and game theoretical approaches. It will be of interest to philosophers, computer scientists and all others interested in the scientific rationality.
Author: Sabina Leonelli
Publisher: Springer Nature
ISBN: 3030371778
Category : Philosophy
Languages : en
Pages : 411
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Book Description
This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.
Author: Roman Egger
Publisher: Springer Nature
ISBN: 3030883892
Category : Business & Economics
Languages : en
Pages : 647
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Book Description
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau
Author: Paolo Mariani
Publisher: Springer Nature
ISBN: 3030512223
Category : Social Science
Languages : en
Pages : 391
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Book Description
The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.
Author: Donald T. Campbell
Publisher: University of Chicago Press
ISBN: 9780226092485
Category : Philosophy
Languages : en
Pages : 644
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Book Description
Selections from the work of an influential contributor to the methodology of the social sciences. He treats: measurement, experimental design, epistemology, and sociology of science each section introduced by the editor, Samuel Overman. Annotation copyright Book News, Inc. Portland, Or.
Author: Cassandra L. Pinnick
Publisher: Rutgers University Press
ISBN: 9780813532271
Category : Philosophy
Languages : en
Pages : 292
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Book Description
This volume presents the first systematic evaluation of a feminist epistemology of sciences' power to transform both the practice of science and our society. Unlike existing critiques, this book questions the fundamental feminist suggestion that purging science of alleged male biases will advance the cause of both science and by extension, social justice. The book is divided into four sections: the strange status of feminist epistemology, testing feminist claims about scientific practice, philosophical and political critiques of feminist epistemology, and future prospects of feminist epistemology. Each of the essays3/4most of which are original to this text3/4 directly confronts the very idea that there could be a feminist epistemology or philosophy of science. Rather than attempting to deal in detail with all of the philosophical views that fall under the general rubric of feminist epistemology, the contributors focus on positions that provide the most influential perspectives on science. Not all of the authors agree amongst themselves, of course, but each submits feminist theories to careful scrutiny. Scrutinizing Feminist Epistemology provides a timely, well-rounded, and much needed examination of the role of gender in scientific research.
Author: K. Brad Wray
Publisher: Cambridge University Press
ISBN: 1139503464
Category : Science
Languages : en
Pages : 245
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Book Description
Kuhn's Structure of Scientific Revolutions (1962) has been enduringly influential in philosophy of science, challenging many common presuppositions about the nature of science and the growth of scientific knowledge. However, philosophers have misunderstood Kuhn's view, treating him as a relativist or social constructionist. In this book, Brad Wray argues that Kuhn provides a useful framework for developing an epistemology of science that takes account of the constructive role that social factors play in scientific inquiry. He examines the core concepts of Structure and explains the main characteristics of both Kuhn's evolutionary epistemology and his social epistemology, relating Structure to Kuhn's developed view presented in his later writings. The discussion includes analyses of the Copernican revolution in astronomy and the plate tectonics revolution in geology. The book will be useful for scholars working in science studies, sociologists and historians of science as well as philosophers of science.
Author: Sharyn Clough
Publisher: Rowman & Littlefield
ISBN: 9780742514652
Category : Literary Criticism
Languages : en
Pages : 182
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Book Description
Feminist thinkers have been critically examining science for over a century; but who critiques the criticism?
