-
Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012)
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- Date
- 14. 2.2012 12:53:22
- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429
-
Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996)
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- Abstract
- It is possible, using ISI's Journal Citation Report (JCR), to calculate average impact factors (AIF) for LCR's subject categories but it can be more useful to know the global Impact Factor (GIF) of a subject category and compare the 2 values. Reports results of a study to compare the relationships between AIFs and GIFs of subjects, based on the particular case of the average impact factor of a subfield versus the impact factor of this subfield as a whole, the difference being studied between an average of quotients, denoted as AQ, and a global average, obtained as a quotient of averages, and denoted as GQ. In the case of impact factors, AQ becomes the average impact factor of a field, and GQ becomes its global impact factor. Discusses a number of applications of this technique in the context of informetrics and scientometrics
- Source
- Journal of information science. 22(1996) no.3, S.165-170
-
Egghe, L.; Rousseau, R.: ¬The influence of publication delays on the observed aging distribution of scientific literature (2000)
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- Abstract
- Observed aging curves are influenced by publication delays. In this article, we show how the 'undisturbed' aging function and the publication delay combine to give the observed aging function. This combination is performed by a mathematical operation known as convolution. Examples are given, such as the convolution of 2 Poisson distributions, 2 exponential distributions, a 2 lognormal distributions. A paradox is observed between theory and real data
- Source
- Journal of the American Society for Information Science. 51(2000) no.2, S.158-165
-
Egghe, L.: Little science, big science and beyond (1994)
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- Source
- Scientometrics. 30(1994) nos.2/3, S.389-392
-
Egghe, L.: ¬A universal method of information retrieval evaluation : the "missing" link M and the universal IR surface (2004)
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- Date
- 14. 8.2004 19:17:22
-
Egghe, L.: On the law of Zipf-Mandelbrot for multi-word phrases (1999)
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- Abstract
- This article studies the probabilities of the occurence of multi-word (m-word) phrases (m=2,3,...) in relation to the probabilities of occurence of the single words. It is well known that, in the latter case, the lae of Zipf is valid (i.e., a power law). We prove that in the case of m-word phrases (m>=2), this is not the case. We present 2 independent proof of this
-
Egghe, L.: ¬The amount of actions needed for shelving and reshelving (1996)
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- Abstract
- Discusses the number of actions (or time) needed to organize library shelves. Studies 2 types pf problem: organizing a library shelf out of an unordered pile of books, and putting an existing shelf of books in the rough order. Uses results from information theory as well as from rank order statistics (runs). Draws conclusions about the advised frequency with which these actions should be undertaken
-
Egghe, L.; Rousseau, R.: ¬A theoretical study of recall and precision using a topological approach to information retrieval (1998)
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- Source
- Information processing and management. 34(1998) nos.2/3, S.191-218
-
Egghe, L.; Rousseau, R.: ¬An h-index weighted by citation impact (2008)
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- Information processing and management. 44(2008) no.2, S.770-780
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Egghe, L.: Properties of the n-overlap vector and n-overlap similarity theory (2006)
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- Abstract
- In the first part of this article the author defines the n-overlap vector whose coordinates consist of the fraction of the objects (e.g., books, N-grams, etc.) that belong to 1, 2, , n sets (more generally: families) (e.g., libraries, databases, etc.). With the aid of the Lorenz concentration theory, a theory of n-overlap similarity is conceived together with corresponding measures, such as the generalized Jaccard index (generalizing the well-known Jaccard index in case n 5 2). Next, the distributional form of the n-overlap vector is determined assuming certain distributions of the object's and of the set (family) sizes. In this section the decreasing power law and decreasing exponential distribution is explained for the n-overlap vector. Both item (token) n-overlap and source (type) n-overlap are studied. The n-overlap properties of objects indexed by a hierarchical system (e.g., books indexed by numbers from a UDC or Dewey system or by N-grams) are presented in the final section. The author shows how the results given in the previous section can be applied as well as how the Lorenz order of the n-overlap vector is respected by an increase or a decrease of the level of refinement in the hierarchical system (e.g., the value N in N-grams).
-
Egghe, L.: Theory of the topical coverage of multiple databases (2013)
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- Abstract
- We present a model that describes which fraction of the literature on a certain topic we will find when we use n (n = 1, 2, .) databases. It is a generalization of the theory of discovering usability problems. We prove that, in all practical cases, this fraction is a concave function of n, the number of used databases, thereby explaining some graphs that exist in the literature. We also study limiting features of this fraction for n very high and we characterize the case that we find all literature on a certain topic for n high enough.
-
Egghe, L.; Rousseau, R.; Hooydonk, G. van: Methods for accrediting publications to authors or countries : consequences for evaluation studies (2000)
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- Source
- Journal of the American Society for Information Science. 51(2000) no.2, S.145-157
-
Egghe, L.: ¬A noninformetric analysis of the relationship between citation age and journal productivity (2001)
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- Abstract
- A problem, raised by Wallace (JASIS, 37,136-145,1986), on the relation between the journal's median citation age and its number of articles is studied. Leaving open the problem as such, we give a statistical explanation of this relationship, when replacing "median" by "mean" in Wallace's problem. The cloud of points, found by Wallace, is explained in this sense that the points are scattered over the area in first quadrant, limited by a curve of the form y=1 + E/x**2 where E is a constant. This curve is obtained by using the Central Limit Theorem in statistics and, hence, has no intrinsic informetric foundation. The article closes with some reflections on explanations of regularities in informetrics, based on statistical, probabilistic or informetric results, or on a combination thereof
-
Egghe, L.; Ravichandra Rao, I.K.: Duality revisited : construction of fractional frequency distributions based on two dual Lotka laws (2002)
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- Abstract
- Fractional frequency distributions of, for example, authors with a certain (fractional) number of papers are very irregular and, therefore, not easy to model or to explain. This article gives a first attempt to this by assuming two simple Lotka laws (with exponent 2): one for the number of authors with n papers (total count here) and one for the number of papers with n authors, n E N. Based an an earlier made convolution model of Egghe, interpreted and reworked now for discrete scores, we are able to produce theoretical fractional frequency distributions with only one parameter, which are in very close agreement with the practical ones as found in a large dataset produced earlier by Rao. The article also shows that (irregular) fractional frequency distributions are a consequence of Lotka's law, and are not examples of breakdowns of this famous historical law.
-
Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005)
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- Abstract
- The discrete Lotka power function describes the number of sources (e.g., authors) with n = 1, 2, 3, ... items (e.g., publications). As in econometrics, informetrics theory requires functions of a continuous variable j, replacing the discrete variable n. Now j represents item densities instead of number of items. The continuous Lotka power function describes the density of sources with item density j. The discrete Lotka function one obtains from data, obtained empirically; the continuous Lotka function is the one needed when one wants to apply Lotkaian informetrics, i.e., to determine properties that can be derived from the (continuous) model. It is, hence, important to know the relations between the two models. We show that the exponents of the discrete Lotka function (if not too high, i.e., within limits encountered in practice) and of the continuous Lotka function are approximately the same. This is important to know in applying theoretical results (from the continuous model), derived from practical data.
-
Egghe, L.: New relations between similarity measures for vectors based on vector norms (2009)
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- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.2, S.232-239
-
Egghe, L.: Influence of adding or deleting items and sources on the h-index (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.370-373
-
Egghe, L.; Guns, R.; Rousseau, R.: Thoughts on uncitedness : Nobel laureates and Fields medalists as case studies (2011)
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- Footnote
- Vgl.: Erratum. In: Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429.
-
Egghe, L.; Guns, R.: Applications of the generalized law of Benford to informetric data (2012)
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- Abstract
- In a previous work (Egghe, 2011), the first author showed that Benford's law (describing the logarithmic distribution of the numbers 1, 2, ... , 9 as first digits of data in decimal form) is related to the classical law of Zipf with exponent 1. The work of Campanario and Coslado (2011), however, shows that Benford's law does not always fit practical data in a statistical sense. In this article, we use a generalization of Benford's law related to the general law of Zipf with exponent ? > 0. Using data from Campanario and Coslado, we apply nonlinear least squares to determine the optimal ? and show that this generalized law of Benford fits the data better than the classical law of Benford.
-
Egghe, L.: ¬The measures precision, recall, fallout and miss as a function of the number of retrieved documents and their mutual interrelations (2008)
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- Source
- Information processing and management. 44(2008) no.2, S.856-876
