Qualitative and Quantitative Analysis of Scientific and Scholarly Communication – serie

This open access book addresses three themes which have been central to Leydesdorff's research: (1) the dynamics of science, technology, and innovation; (2) the scientometric operationalization of these concept; and (3) the elaboration in terms of a Triple Helix of university-industry-government relations. In this study, I discuss the relations among these themes. Using Luhmann's social-systems theory for modelling meaning processing and Shannon's theory for information processing, I show that synergy can add new options to an innovation system as redundancy. The capacity to develop new options is more important for innovation than past performance. Entertaining a model of possible future states makes a knowledge-based system increasingly anticipatory. The trade-off between the incursion of future states on the historical developments can be measured using the Triple-Helix synergy indicator. This is shown, for example, for the Italian national and regional systems of innovation.

This is the first volume of a book series dedicated to "Qualitative and Quantitative Analysis of Scientific and Scholarly Communication". Fighting plagiarism is a the top priority for STM publishing. A practical guide will importantly contribute to the awareness of the relevant communities, bringing to the surface the basic rules and examples from the literature.

This is the first volume of a book series dedicated to "Qualitative and Quantitative Analysis of Scientific and Scholarly Communication". Fighting plagiarism is a the top priority for STM publishing. A practical guide will importantly contribute to the awareness of the relevant communities, bringing to the surface the basic rules and examples from the literature.

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.

This book presents an introduction to the field of applied evaluative informetrics, dealing with the use of bibliometric or informetric indicators in research assessment. It sketches the field’s history, recent achievements, and its potential and limits. The book dedicates special attention to the application context of quantitative research assessment. It describes research assessment as an evaluation science, and distinguishes various assessment models, in which the domain of informetrics and the policy sphere are disentangled analytically. It illustrates how external, non-informetric factors influence indicator development, and how the policy context impacts the setup of an assessment process. It also clarifies common misunderstandings in the interpretation of some often used statistics.Addressing the way forward, the book expresses the author’s critical views on a series of fundamental problems in the current use of research performance indicators in research assessment. Highlighting the potential of informetric techniques, a series of new features is proposed that could be implemented in future assessment processes. It sketches a perspective on altmetrics and proposes new lines in longer term, strategic indicator research. It is written for interested scholars from all domains of science and scholarship, and especially for all those subjected to research assessment, research students at advanced master and PhD level, research managers, funders and science policy officials, and to practitioners and students in the field.

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.