The skewness of computer science

arXiv:0912.4188v2 [cs.DL] 15 Feb 2010 The skewness of computer science Massimo Franceschet Department of Mathematics and Computer Science, University of Udine Via delle Scienze 206 – 33100 Udine, Italy massimo.franceschet@dimi.uniud.it Abstract Computer science is a relatively young discipline combining science, engineer- ing, and mathematics. The main flavors of computer science research involve the theoretical development of conceptual models for the different aspects of com- puting and the more applicative building of software artifacts and assessment of their properties. In the computer science publication culture, conferences are an important vehicle to quickly move ideas, and journals often publish deeper versions of papers already presented at conferences. These peculiarities of the discipline make computer science an original research field within the sciences, and, therefore, the assessment of classical bibliometric laws is particularly im- portant for this field. In this paper, we study the skewness of the distribution of citations to papers published in computer science publication venues (jour- nals and conferences). We find that the skewness in the distribution of mean citedness of different venues combines with the asymmetry in citedness of arti- cles in each venue, resulting in a highly asymmetric citation distribution with a power law tail. Furthermore, the skewness of conference publications is more pronounced than the asymmetry of journal papers. Finally, the impact of jour- nal papers, as measured with bibliometric indicators, largely dominates that of proceeding papers. Key words: Research evaluation; Bibliometric indicators; Citation distributions; Power law distributions. 1. Introduction Computer science is an original discipline combining engineering and natural sciences as well as mathematics. It concerns itself with the representation and processing of information using algorithmic techniques. Research in computer science includes two main flavors: Theory, developing conceptual frameworks for understanding the many aspects of computing, and Systems, building software artifacts and assessing their properties (Choppy et al., 2009). A distinctive feature of computer science publication is the importance of prestigious conferences. Acceptance rates at selective computer science confer- ences range between 10% and 20%; for instance, in 2007-2008, ICSE (software Preprint submitted to Information Processing & Management June 10, 2018 engineering) 13%, OOPSLA (object technology) 19%, POPL (programming languages) 18%. Journals have their role, but do not necessarily carry more prestige. The story of the development of computer science conferences is well reported by Fortnow (2009), page 33: The growth of computers in the 1950s led nearly every major uni- versity to develop a strong computer science discipline over the next few decades. As a new field, computer science was free to experi- ment with novel approaches to publication not hampered by long tra- ditions in more established scientific and engineering communities. Computer science came of age in the jet age where the time spent traveling to a conference no longer dominated the time spent at the conference itself. The quick development of this new field required rapid review and distribution of results. So the conference system quickly developed, serving the multiple purposes of the distribution of papers through proceedings, presentations, a stamp of approval, and bringing the community together. These peculiarities of the field – the dualities Theory/System and Jour- nal/Conference – make computer science an original discipline within the sci- ences (Denning, 2005). It is, therefore, interesting to investigate how these distinctive features of the discipline impact on the classical laws of informet- rics (Bookstein, 1990), e.g., Lotka’s Law of scientific productivity (Lotka, 1926), Bradford’s Law of scatter (Bradford, 1934), and skewness of citations to sci- entific publications (Seglen, 1992). In the present contribution, we study the skewness of the citation distribution of computer science papers. We distinguish between journal and conference papers, exploiting the Conference Proceeding index recently added by Thomson Reuters to Web of Science. We furthermore tackle the problem of finding a theoretical model that well fits the empirical citation distributions. Finally, we compare the strength of impact of journal and proceeding papers as measured with bibliometric indicators, including the highly celebrated Hirsch index (Hirsch, 2005). The outline of the paper is as follows. In Section 2 we study the shape of the citation distributions of computer science journal and conference papers. Section 3 is devoted to the finding of a theoretical model that well fits such citation distributions. Finally, in Section 4 we summarize our findings and their implications. 2. The skewness of citation distributions Is the distribution of citations to computer sci

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