The Challenge Of Scientometrics

Scientometrics--the quantitative study of scientific communication--challenges science and technology studies by demonstrating that organized knowledge production and control is amenable to measurement. First, the various dimensions of the empirical study of the sciences are clarified in a methodological analysis of theoretical traditions, including the sociology of scientific knowledge and neo-conventionalism in the philosophy of science. Second, the author argues why the mathematical theory of communication enables us to address crucial problems in science and technology studies, both on the qualitative side (e.g., the significance of a reconstruction) and on the quantitative side (e.g., the prediction of indicators). A comprehensive set of probabilistic entropy measures for studying complex developments in networks is elaborated. In the third part of the study, applications to S&T policy questions (e.g., the emergence of a European R&D system), to problems of (Bayesian) knowledge representations, and to the study of the sciences in terms of 'self-organizing' paradigms of scientific communication are provided. A discussion of directions for further research concludes the study.

Scientometrics for the Humanities and Social Sciences is the first ever book on scientometrics that deals with the historical development of both quantitative and qualitative data analysis in scientometric studies. It focuses on its applicability in new and emerging areas of inquiry. This important book presents the inherent potential for data mining and analysis of qualitative data in scientometrics. The author provides select cases of scientometric studies in the humanities and social sciences, explaining their research objectives, sources of data and methodologies. It illustrates how data can be gathered not only from prominent online databases and repositories, but also from journals that are not stored in these databases. With the support of specific examples, the book shows how data on demographic variables can be collected to supplement scientometric data. The book deals with a research methodology which has an increasing applicability not only to the study of science, but also to the study of the disciplines in the humanities and social sciences.

This book deals with methods to evaluate scientific productivity. In the book statistical methods, deterministic and stochastic models and numerous indexes are discussed that will help the reader to understand the nonlinear science dynamics and to be able to develop or construct systems for appropriate evaluation of research productivity and management of research groups and organizations. The dynamics of science structures and systems is complex, and the evaluation of research productivity requires a combination of qualitative and quantitative methods and measures. The book has three parts. The first part is devoted to mathematical models describing the importance of science for economic growth and systems for the evaluation of research organizations of different size. The second part contains descriptions and discussions of numerous indexes for the evaluation of the productivity of researchers and groups of researchers of different size (up to the comparison of research productivities of research communities of nations). Part three contains discussions of non-Gaussian laws connected to scientific productivity and presents various deterministic and stochastic models of science dynamics and research productivity. The book shows that many famous fat tail distributions as well as many deterministic and stochastic models and processes, which are well known from physics, theory of extreme events or population dynamics, occur also in the description of dynamics of scientific systems and in the description of the characteristics of research productivity. This is not a surprise as scientific systems are nonlinear, open and dissipative.

Technological change is one of the greatest issues in the modern world. As the world faces societal challenges, e.g., climate challenges, aging problem, and energy security, technology will contribute to new or better solutions for those problems. New technologies take time to develop and mature; moreover, they tend to be born in the gaps of multiple technology fields; therefore, early detection of emerging technological concepts across multiple disciplines will be a very important issue. Our goal seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technological emergence using one of the major foresight exercises, scientometrics. There is now a huge flood of scientific and technical information, especially scientific publications and patent information. Using the information patterns of emergence for technological concepts has been discovered and theories of technical emergence have been also developed in several years. We have been developing visualization tools in which thousands of technical areas have been interacted with each other and evolved in time. Several indicators of technical emergence have been improved by universities, international organizations, and funding agencies. This book intends to provide readers with a comprehensive overview of the current state of the art in scientometrics that focuses on the systematic, continuous, and comprehensive assessment of technological emergence.

The twenty-first century brought unique developments in science and technology. Research surged as individuals sought to uncover hidden knowledge, leading to the introduction of research evaluation to ensure precise and fair research output and dissemination. Scholarly Content and Its Evolution by Scientometric Indicators: Emerging Research and Opportunities is a pivotal reference source that provides vital research on the application of research evaluation, specifically through the lens of scientometrics. While highlighting topics such as bibliometrics and the h-index, this publication explores a full range of research indicators available for the evaluation and assessment of scientific literature. This book is ideally designed for scholars, professors, academicians, researchers, and graduate-level students seeking current research on metric science.

Can the methods of science be directed toward science itself? How did it happen that scientists, scientific documents, and their bibliographic links came to be regarded as mathematical variables in abstract models of scientific communication? What is the role of quantitative analyses of scientific and technical documentation in current science policy and management? Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics answers these questions through a comprehensive overview of theories, techniques, concepts, and applications in the interdisciplinary and steadily growing field of bibliometrics. Since citation indexes came into the limelight during the mid-1960s, citation networks have become increasingly important for many different research fields. The book begins by investigating the empirical, philosophical, and mathematical foundations of bibliometrics, including its beginnings with the Science Citation Index, the theoretical framework behind it, and its mathematical underpinnings. It then examines the application of bibliometrics and citation analysis in the sciences and science studies, especially the sociology of science and science policy. Finally it provides a view of the future of bibliometrics, exploring in detail the ongoing extension of bibliometric methods to the structure and dynamics of the World Wide Web. This book gives newcomers to the field of bibliometrics an accessible entry point to an entire research tradition otherwise scattered through a vast amount of journal literature. At the same time, it brings to the forefront the cross-disciplinary linkages between the various fields (sociology, philosophy, mathematics, politics) that intersect at the crossroads of citation analysis. Because of its discursive and interdisciplinary approach, the book is useful to those in every area of scholarship involved in the quantitative analysis of information exchanges, but also to science historians and general readers who simply wish to familiarize them

This book presents the latest research, theoretical methods, and novel applications in the field of Health 5.0. The authors focus on combating COVID-19 or other pandemics through facilitating various technological services. The authors discuss new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 or other pandemic based on machine intelligence models and communication technologies. The aim of the coverage is to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence and Internet of Medical Things (IoMT). This book emphasizes the need to analyze all the information through studies and research carried out in the field of computational intelligence, communication networks, and presents the best solutions to combat COVID and other pandemics.

During the so-called ‘historical turn’ in the philosophy of science, philosophers and historians boldly argued for general patterns throughout the history of science. From Kuhn’s landmark "Structure of Scientific Revolutions" until the "Scrutinizing Science" project led by Larry Laudan, there was optimism that there could be a general theoretical approach to understanding the process of scientific change. This optimism gradually faded as historians and philosophers began to focus on the details of specific case studies located within idiosyncratic historical, cultural, and political contexts, and abandoned attempts to uncover general patterns of how scientific theories and methods change through time. Recent research has suggested that while we have learned a great deal about the diversity and complexity of scientific practices across history, the push to abandon hope for a broader understanding of scientific change was premature. Because of this, philosophers, historians, and social scientists have become interested in reviving the project of understanding the mechanism of scientific change while respecting the diversity and complexity that has been unveiled by careful historical research over the past few decades. The chapters in this volume consider a particular proposal for a general theory of how scientific theories and methods change over time, first articulated by Hakob Barseghyan in "The Laws of Scientific Change" and since developed in a series of papers by a variety of members of the scientonomy community. The chapters consider a wide range of issues, from conceptual and historical challenges to the posited intellectual patterns in the history of science, to the possibility of constructing a general theory of scientific change, to begin with. Offering a new take on the project of constructing a theory of scientific change and integrating historical, philosophical, and social studies of science, this volume will be of interest to historians, philosophers, and sociologists of science.