The Challenge of Scientometrics

The Development, Measurement, and Self-Organization of Scientific Communications

by Loet Leydesdorff


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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.

About The Author

Loet Leydesdorff (Ph.D. sociology, M.A. philosophy, and M.Sc. biochemistry) is Senior Lecturer at the Department of Communication Studies of the University of Amsterdam. He has published extensively in the philosophy of science, social network analysis, scientometrics, and the sociology of innovation.

This study can also be considered as the methodological complement to A Sociological Theory of Communications: The Self-Organization of the Knowledge-Based Society (Universal Publishers, 2000). The two books provide theory and methods for the investigation of the knowledge base in socio-cognitive processes of communication and codification.

Also by Loet Leydesdorff: The Knowledge-Based Economy: Modeled, Measured, Simulated (Universal Publishers, 2006);