The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and collaboration patterns for papers by analyzing its behavior at the level of a national science system. We analyzed 3012 Cuban articles on Natural Sciences that received 17,295 citations. The number of articles published through collaboration accounted for 94%. The collaborative articles accounted for 96% of overall citations. The citation-based performance and international collaboration patterns exhibit a power-law correlation with a scaling exponent of 1.22 ± 0.08. Citations to a field’s research internationally collaborative articles in Natural Sciences tended to increase 2^1.22 or 2.33 times each time it doubles the number of internationally collaborative papers. The Matthew Effect is stronger for internationally collaborative papers than for domestic collaborative articles.
The objective of this article is to determine if academic collaboration is associated with the citation-based performance of articles that are published in management journals. We analyzed 127,812 articles published between 1988 and 2013 in 173 journals on the ISI Web of Science in the “management” category. Collaboration occurred in approximately 60% of all articles. A power–law relationship was found between citation-based performance and journal size and collaboration patterns.The number of citations expected by collaborative articles increases 2^1.89 or 3.7 times when the number of collaborative articles published in a journal doubles. The number of citations expected by non-collaborative articles only increases 2^1.35 or 2.55 times if a journal publishes double the number of non-collaborative articles. The Matthew effect is stronger for collaborative than for non-collaborative articles. Scale-independent indicators increase the confidence in the evaluation of the impact of the articles published in management journals.
The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and collaboration patterns for papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through collaboration account for 89%. The citation-based performance and collaboration patterns exhibit a power-law correlation with a scaling exponent of 1.20 ± 0.07. Citations to a subfield’s research articles tended to increase 2.1.20 or 2.30 times each time it doubles the number of collaborative papers. The scaling exponent for the power-law relationship for single-authored papers was 0.85 ± 0.11. The citations to a subfield’s single-authored research articles increased 2.0.85 or 1.89 times each time the research area doubles the number of non-collaborative papers. The Matthew effect is stronger for collaborated papers than for single-authored. In fact, with a scaling exponent < 1.0 the impact of single-author papers exhibits a cumulative disadvantage or inverse Matthew effect.
Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kxα, where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too.
The tension between equity and excellence is fundamental in science policy. This tension might appear to be resolved through the use of merit-based evaluation as a criterion for research funding. This is not the case. Merit-based decision making alone is insufficient because of inequality aversion, a fundamental tendency of people to avoid extremely unequal distributions. The distribution of performance in science is extremely unequal, and no decision maker with the power to establish a distribution of public money would dare to match the level of inequality in research performance. We argue that decision makers who increase concentration of resources because they accept that research resources should be distributed according to merit probably implement less inequality than would be justified by differences in research performance. Here we show that the consequences are likely to be suppression of incentives for the very best scientists. The consequences for the performance of a national research system may be substantial. Decision makers are unaware of the issue, as they operate with distributional assumptions of normality that guide our everyday intuitions.
Do researchers produce scientific and technical knowledge differently than they did ten years ago? What will scientific research look like ten years from now? Addressing such questions means looking at science from a dynamic systems perspective. Two recent books about the social system of science, by Ziman and by Gibbons, Limoges, Nowotny, Schwartzman, Scott, and Trow, accept this challenge and argue that the research enterprise is changing. This article uses bibliometric data to examine the extent and nature of changes identified by these authors, taking as an example British research. We use their theoretical frameworks to investigate five characteristics of research said to be increasingly pervasive-namely, application, interdisciplinarity, networking, internationalization, and concentration of resources. Results indicate that research may be becoming more interdisciplinary and that research is increasingly conducted more in networks, both domestic and international; but the data are more ambiguous regarding application and concentration. CR - Copyright © 1996 Sage Publications, Inc.
Chinese national and regional innovation systems
Economic transition This paper uses scale-independent indicators to explore the Chinese national and regional innovation systems during economic transition. Our perception of an innovation system is frequently informed by conventional indicators based on linear assumptions while actually innovation systems may behave differently. Scale-independent indicators characterize non-linear properties of an innovation system. They can give decision makers deeper insight into the dynamics of innovation systems, and they may lead to more practical public policies [Katz, J. S. (2006). Indicators for complex innovation systems. Research Policy, 35, 893-909]. As reported for the European and Canadian innovation systems the Chinese systems exhibited scaling correlations between GERD (Gross Expenditure on Domestic R&D) and GDP (Gross Domestic Product) over time and at points in time. The scaling factors of the correlations tell us that between 1995 and 2005 the Chinese GERD exhibited a strong non-linear tendency to increase with GDP. Furthermore they show that the GERD of the Western region is growing much slower than its GDP as compared with Eastern and Central regions. This observation has policy implications suggesting further improvements need to be made to the research infrastructure and funding of the Western region. The GDP-POP (Population) scaling factor shows that the [`]wealth intensity' or GDP per capita is increasing much faster than the exponential growth of the Chinese population. In contrast the systemic GDP-POP scaling factor shows that regional development is non-linear. Finally, the paper-GDP and patent-GDP scaling factors tell us that outputs of science and technology for China are growing faster than economic growth. The systemic paper-GDP and patent-GDP scaling factors show that the growth rates are uneven across the provinces.
The aim of the Science Foresight Project was to design and assess a simple, objective and costeffective technique to gather information about emerging short and long-term research
developments, primarily in the physical and engineering sciences. International experts were
objectively chosen using co-citation patterns in scientific and technical literature, and were invited
to submit their predictions about emerging developments in their research fields. They were
questioned about how the effects of various factors and driving forces might affect their predictions.
The cost and time required to administer the questionnaire and collect the responses was
minimised through the use of Internet and Web based technologies. A simple process was used to
report the predictions; short excerpts from each prediction were used as the summary and each
prediction was classified into one of ten categories of emerging developments. Authors from 114
papers (23.7%) responded, identifying a total of 190 short-term and 111 long-term predicted
emerging developments. Expert responses were received from an international group of senior
researchers between the ages of 36 and 55, mostly engaged in basic research in academic
institutions. Some experts described specific emerging developments, some discussed broad
emerging trends in their field and others described both. Emerging development categories such
as Atomic & Stellar Matter, Biology & Biosphere, Biomedical & Clinical, Computers & Robotics and
Genomics & Proteomics were closely aligned with conventional science areas while other
categories such as Mathematical & Computational and Nano Science & Technology contained
predictions from almost every area of science. The technique developed and applied here appears
to constitute an efficient means of surveying the international research community in order to gain
insights into common patterns that evolve from their collective research activities. Dynamically
monitoring emerging research developments on a continuous basis could provide valuable
information to policy makers, planners and researchers
Although there have been many previous studies of research collaboration, comparatively little attention has been given to the concept of ‘collaboration’ or to the adequacy of attempting to measure it through co-authorship. In this paper, we distinguish between collaboration at different levels and show that inter-institutional and international collaboration need not necessarily involve inter-individual collaboration. We also show that co-authorship is no more than a partial indicator of collaboration. Lastly, we argue for a more symmetrical approach in comparing the costs of collaboration with the undoubted benefits when considering policies towards research collaboration.
This report was commissioned by the UK MOD’s Research Acquisition Organisation as
part of a wider investigation aimed at informing the selection of an appropriate process
for ‘horizon scanning’. Though the stakeholders in the output of such scanning extend
throughout MOD and into its wider networks within government and commercial
organisations, the process must primarily serve Output 5 of MOD’s research programme.
In this context, horizon scanning can be seen as the means for identifying significant
developments in civil and foreign defence science and technology that may not otherwise
be noted through MOD’s own technical programmes.
Through the ‘Science Foresight’ Project, which reported in 2001, MOD/Dstl, aided by
SPRU, designed and piloted a process that addresses at least some of the aspects required
for horizon scanning as defined above. Specifically, this process provides a means for
‘targeting’ influential scientific authors and collecting their views as to the future of their
domain.
Performance indicators such as national wealth (GDP per capita), R&D intensity (GERD/GDP) and scientific impact (citations/paper) are used to compare innovation systems. These indicators are derived from the ratio of primary measures such as population, GDP, GERD and papers. Frequently they are used to rank members of an innovation system and to inform decision makers. This is illustrated by the European Research Area S&T indicators scoreboard used to compare the performance of member states. A formal study of complex systems has evolved over the past few decades from common observations made by researchers from many fields. Complex systems are dynamic and many of their properties emerge from the interactions among the entities in them. They also have a propensity to exhibit power law or scaling correlations between primary measures used to characterize them. Katz [Katz, J.S., 2000. Scale independent indicators and research assessment. Science and Public Policy 27, 2336] showed that scientific impact (citations/paper) scales with the size of the group (papers). In this paper it will be shown that two other common measures, R&D intensity and national wealth, scale with the sizes of European countries and Canadian provinces. Some of these scaling correlations are predictable. These findings illustrate that a performance indicator derived from the ratio of two measures may not be properly normalized for size. This paper argues that innovation systems are complex systems. Hence scaling correlations are expected to exist between the primary measures used to characterize them. These scaling correlations can be used to construct scale-independent (scale-adjusted) indicators and models that are truly normalized for size. Scale-independent indicators can more accurately inform decision makers how groups of different sizes contribute to an innovation system. The ranks of member groups of an innovation system by scaleindependent indicators can be subtly and profoundly different than the ranks given by conventional indicators. The differences can result in a shift in perspective about the performance of members of an innovation system that has public policy implications.
The Web and innovation systems are interacting
complex systems that impact each other. Web
indicators used to inform decision-makers about
the impact they are having on each must be reproducible and relevant. Web metrics research is
young and best-practice methodologies for producing robust indicators are evolving. This study
describes a methodology for producing robust
indicators of the web presence of the European
and Canadian innovation systems. It demonstrates that the emergent properties and scaling
characteristics expected of complex systems are
captured by these indicators. It illustrates how
these indicators can be used to measure the
amount of recognition a nation or province’s
web presence receives from other nations and
provinces in their innovation systems.
Technical Report for the EC Web Indicators for Scientific, Technological and Innovation Research (WISER) project.