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  • × author_ss:"Burrell, Q.L."
  • × year_i:[2000 TO 2010}
  1. Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.06 
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    Abstract
    Citation rates are becoming increasingly important in judging the research quality of journals, institutions and departments, and individual faculty. This paper looks at the pattern of citations across different management science journals and over time. A stochastic model is proposed which views the generating mechanism of citations as a gamma mixture of Poisson processes generating overall a negative binomial distribution. This is tested empirically with a large sample of papers published in 1990 from six management science journals and found to fit well. The model is extended to include obsolescence, i.e., that the citation rate for a paper varies over its cited lifetime. This leads to the additional citations distribution which shows that future citations are a linear function of past citations with a time-dependent and decreasing slope. This is also verified empirically in a way that allows different obsolescence functions to be fitted to the data. Conclusions concerning the predictability of future citations, and future research in this area are discussed.
    Date
    26.12.2007 19:22:05
    Source
    Information processing and management. 42(2006) no.6, S.1451-1464
  2. Burrell, Q.L.: Extending Lotkaian informetrics (2008) 0.03 
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    Abstract
    The continuous version of the Lotka distribution, more generally referred to outside of informetrics as the Pareto distribution, has long enjoyed a central position in the theoretical development of informetrics despite several reported drawbacks in modelling empirical data distributions, most particularly that the inverse power form seems mainly to be evident only in the upper tails. We give a number of published examples graphically illustrating this shortcoming. In seeking to overcome this, we here draw attention to an intuitively reasonable generalization of the Pareto distribution, namely the Pareto type II distribution, of which we consider two versions. We describe its basic properties and some statistical features together with concentration aspects and argue that, at least in qualitative terms, it is better able to describe many observed informetric phenomena over the full range of the distribution. Suggestions for further investigations, including truncated and time-dependent versions, are also given.
    Source
    Information processing and management. 44(2008) no.5, S.1794-1807
  3. Burrell, Q.L.: Measuring similarity of concentration between different informetric distributions : two new approaches (2005) 0.01 
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    Abstract
    From its earliest days, much investigative work in informetrics has been concerned with inequality aspects. Beginning with the weIl-known Gin! coefficient as a measure of the concentration/inequality of productivity within a single data set, in this study we look at the problem of measuring relative inequallty of productivity between two data sets. A measure originally proposed by Dagum (1987), analogous to the Gin! coefficient, is discussed and developed with both theoretical and empir!cal illustrations. From this we derive a standardized measure-the relative concentration coefficient-based an the notion of "relative economic affluence" also introduced by Dagum (1987). Finally, a new standardized measure-the co-concentration coefficient, in some ways analogous to the correlation coefficient-is defined. The merits and drawbacks of these two measures are discussed and illustrated. Their value will be most readily appreclated in comparative empirical studies.
  4. Burrell, Q.L.: "Ambiguity" ans scientometric measurement : a dissenting view (2001) 0.01 
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    Abstract
    Abe Bookstein has long been a persuasive advocate of the central role of the classical Lotka-Bradford-Zipf "laws" in bibliometrics and, subsequently, scientometrics and informetrics. In a series of often-quoted papers (Bookstein, 1977, 1990a, 1990b, 1997), he has sought to demonstrate that "Lotka-type" laws have a unique resilience to various forms of reporting, which leads inevitably and naturally to their observance in empirical informetric data collected under a wide variety of circumstances. A general statement of his position was featured in the recent JASIST Special Topic Issue on Information Science at the Millennium (Bookstein, 2001). We shall argue that there are grounds to dispute some of the logic, the mathematics, and the reality of the development. The contention is on the one hand that Bookstein's development lacks a rigorous mathematical basis, and on the other, that, in general, informetric processes are adequately described within a standard probabilistic framework with stochastic modelling offering the more productive approach.
  5. Burrell, Q.L.: Predicting future citation behavior (2003) 0.01 
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    Date
    29. 3.2003 19:22:48

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