Journal of the American Society for Information Science and Technology 57(11) (2006) 1470-1486 / Published in Journal of the American Society for Information Science and Technology 57(11) (2006) 1470-1486. Abstract: Logarithmic transformation of the data has been recommended by the literature in the case of highly skewed distributions such as those commonly found in information science. The purpose of the transformation is to make the data conform to the lognormal law of error for inferential purposes. How does this transformation affect the analysis? We factor analyze and visualize the citation environment of the Journal of the American Chemical Society (JACS) before and after a logarithmic transformation. The transformation strongly reduces the variance necessary for classificatory purposes and therefore is counterproductive to the purposes of the descriptive statistics. We recommend against the logarithmic transformation when sets cannot be defined unambiguously. The intellectual organization of the sciences is reflected in the curvilinear parts of the citation distributions, while negative powerlaws fit excellently to the tails of the distributions.

Institutions and their aggregates are not the right units of analysis for developing a science policy with cognitive goals in view. Institutions, however, can be compared in terms of their performance with reference to their previous stages. Kingâ s (2004) â The scientific impact of nationsâ has provided the data for this comparison. Evaluation of the data from this perspective along the time axis leads to completely different and hitherto overlooked conclusions: a new dynamic can be revealed which points to a group of emerging nations. These nations do not increase their contributions marginally, but their national science systems grow endogenously. In addition to publications, their citation rates keep pace with the exponential growth patterns, albeit with a delay. The center of gravity of the world system of science may be changing accordingly.

China has become the fifth leading nation in terms of its share of the worldâ s scientific publications. The citation rate of papers with a Chinese address for the corresponding author also exhibits exponential growth. More specifically, China has become a major player in critical technologies like nanotechnology. Although it is difficult to delineate nanoscience and nanotechnology, we show that China has recently achieved a position second only to that of the USA. Funding for R&D has been growing exponentially, but since 1997 even more in terms of business expenditure than in terms of government expenditure. It seems that the Chinese government has effectively used the public-sector research potential to boost the knowledge-based economy of the country. Thus, China may be achieving the (â Lisbonâ ) objectives of the transition to a knowledge-based economy more broadly and rapidly than its western counterparts. Because of the sustained increase in Chinese government funding and the virtually unlimited reservoir of highly-skilled human resources, one may expect a continuation of this growth pattern in the near future.

Based on the Science Citation Index - Expanded web-version, the USA is still by far the strongest nation in terms of scientific performance. Its relative decline in percentage share of publications is largely due to the emergence of China and other Asian nations. In terms of citations, the competitive advantage of the American "domestic market" is diminished, while the European Union (EU) is profiting more from the enlargement of the database over time than the US. However, the USA is still outperforming all other countries in terms of highly cited papers and citation/publication ratios, and it is more successful than the EU in coordinating its research efforts in strategic priority areas like nanotechnology. In this field, the People's Republic of China (PRC) has become second largest in 2005 in both numbers of papers published and citations behind the USA.

Evaluation criteria can be expected to differ with the institutional perspectives in university-industry-government relations. How can one use evaluation for the improvement of the innovative capacity of these networks? Indicators used for the evaluation, can be specified as variables in a model. The model can be used, among other things, to distinguish between intended and unintended outcomes of the practices under study. Institutionalized arrangements generate filters which stimulate innovation selectively. A focus on failures is fruitful for knowledge-based innovation since it allows for the further specification of expectations. The latter can also be turned into research questions.

Since 1992, a new Chinese innovation system has been emerging in terms of university-industry-government relations. In recent years, science parks, incubators, and high-tech development zones have been provided with strong incentives. The commitment of the Chinese government to the further introduction of a market economy has been elaborated with a focus on the knowledge-base. The model of institutional adjustments has been replaced with systemic and evolutionary thinking about niche development and human resource management. Government interventions remain important for interfacing knowledge-based developments with those on the market. For example, new legislation on issues of â intellectual property rightsâ is crucial to the absorption of venture capital into these developments.

Research evaluation is based on a representation of the research. Improving the quality of the representations cannot prevent the indicators from being provided with meaning by a receiving discourse different from the research system(s) under study. Since policy decisions affect the systems under study, this configuration generates a tension that has been driving the further development of science indicators since World War II. The article discusses historically the emergence of science indicators and some of the methodological problems involved. More recent developments have been induced by the emergence of the European Union as a supra-national level of policy coordination and by the Internet as a global medium of communication. As science, technology, and innovation policies develop increasingly at various levels and with different objectives, the evaluative discourses can be expected to differentiate with reference to the discourses in which they are enrolled.

Forthcoming in special issue of Research Policy Vol. 35 (2006) / When two selection environments operate upon each other, mutual shaping in a co-evolution along a particular trajectory is one possible outcome. When three selection environments are involved, more complex dynamics can be expected as a result of interactions involving bi-lateral and tri-lateral relations. Three selection environments are specified in the Triple Helix model: (1) wealth generation (industry), (2) novelty production (academia), and (3) public control (government). Furthermore, this model somewhat reduces the complexity by using university-industry-government relations for the specification of the historical conditions of the non-linear dynamics. Whereas the historical analysis informs us about how institutions and institutional arrangements carry certain functions, the evolutionary analysis focuses on the functions of selection environments in terms of outputs. One can no longer expect a one-to-one correspondence between institutions and functions; a statistics is needed for the evaluation of how, for how long, and to what extent institutional arrangements enhance synergies among different selection environments. The empirical contributions to this Triple Helix issue point in the direction of â rich ecologiesâ : the construction of careful balances between differentiation and integration among the three functions.

Cognitive Science 30(6), 2006 / From its inception, a large part of the motivation for Cognitive Science has been the need for an interdisciplinary journal for the study of minds and intelligent systems. In the inaugural editorial for the journal, Allan Collins (1977) wrote â Current journals are fragmented along old disciplinary lines, so there is no common place for workers who approach these problems from different disciplines to talk to each otherâ (p. 1). The interdisciplinarity of the journal has served a valuable cross-fertilization function for those who read the journal to discover articles written for and by practitioners across a wide range of fields. The challenges of building and understanding intelligent systems are sufficiently large that they will most likely require the skills of psychologists, computer scientists, philosophers, educators, neuroscientists, and linguists collaborating and coordinating their efforts. One threat to the interdisciplinarity of Cognitive Science, both the field and journal, is that it may become, or already be, too dominated by psychologists (Schunn, Crowley, & Okada, 1998; Von Eckardt, 2001). One piece of evidence supporting this contention is that many of the manuscripts submitted to Cognitive Science are given â psychologyâ as field keyword by their authors. In 2005, psychology was a keyword for 51% of submissions, followed distantly by linguistics (17%), artificial intelligence (13%), neuroscience (10%), computer science (9%), and philosophy (8%) (these percentages sum to more than 100% because authors are not restricted to designating only a single field). Another quantitative way to assess the interdisciplinarity of Cognitive Science as well as its general intellectual niche is to analyze aggregated journal-journal citations. The Institute for Scientific Information (ISI) gathers data not only on how individual articles cite one another, but also on macroscopic citation patterns among journals. Journals or sets of journals can be considered as proxies for fields. As fields become established, they often create journals (Leydesdorff, Cozzens, & Van den Besselaar, 1994). As Collins (1977) wrote when launching Cognitive Science, â In starting the journal we are just adding another trapping in the formation of a new disciplineâ (p. 1). By studying the patterns of citations among journals that cite and are cited by Cognitive Science, we can better: 1) appreciate the scholarly ecology surrounding the journal and the journalâ s role within this ecology, 2) establish competitor and alternate journals, and 3) determine the natural clustering of fields related to cognitive science (Leydesdorff, 2006; forthcoming).

Regions can be considered as "regional innovation systems," but the question of whether and to what extent technology transfer is taking place at this or other (e.g., national and global) levels remains empirical. The theme issue contains a number of case studies of "regional innovation systems" within the European Union. Other papers elaborate on the pros and cons of the systemic approach to the technology transfer processes involved, or make comparisons across regions. In this introduction, the editors discuss the relations between regional policies, technology and innovation policies, and the integration of these different aspects into (potentially regional) systems of innovation. Under what conditions can "technology transfer" be considered as a mechanism of integration at the regional level?