Hamid R. Ekbia

Cassidy R. Sugimoto

Michael Mattioli

Big Data Is Not a Monolith

Vincent Larivière

Dismantling Systemic Barriers to Advancement

Equity for Women in Science

Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. Yet, researchers and practitioners in this field have lacked clear theories to guide their work. As early as 1981, then doctoral student Blaise Cronin published "The need for a theory of citing" —a call to arms for the fledgling scientometric community to produce foundational theories upon which the work of the field could be based. More than three decades later, the time has come to reach out the field again and ask how they have responded to this call. This book compiles the foundational theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact.

Theories of Informetrics and Scholarly Communication

Policy makers, academic administrators, scholars, and members of the public are clamoring for indicators of the value and reach of research. The question of how to quantify the impact and importance of research and scholarly output, from the publication of books and journal articles to the indexing of citations and tweets, is a critical one in predicting innovation, and in deciding what sorts of research is supported and whom is hired to carry it out. There is a wide set of data and tools available for measuring research, but they are often used in crude ways, and each have their own limitations and internal logics. Measuring Research: What Everyone Needs to Know� will provide, for the first time, an accessible account of the methods used to gather and analyze data on research output and impact. Following a brief history of scholarly communication and its measurement -- from traditional peer review to crowdsourced review on the social web -- the book will look at the classification of knowledge and academic disciplines, the differences between citations and references, the role of peer review, national research evaluation exercises, the tools used to measure research, the many different types of measurement indicators, and how to measure interdisciplinarity. The book also addresses emerging issues within scholarly communication, including whether or not measurement promotes a "publish or perish" culture, fraud in research, or "citation cartels." It will also look at the stakeholders behind these analytical tools, the adverse effects of these quantifications, and the future of research measurement.

What Everyone Needs to Know

Measuring Research

Blaise Cronin

A comprehensive, state-of-the-art examination of the changing ways we measure scholarly performance and research impact.

Harnessing Multidimensional Indicators of Scholarly Impact

Beyond Bibliometrics

Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West

Cassidy R. Sugimoto

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