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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
- Date
- 29. 9.2001 13:59:34
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Egghe, L.: ¬A universal method of information retrieval evaluation : the "missing" link M and the universal IR surface (2004)
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- Abstract
- The paper shows that the present evaluation methods in information retrieval (basically recall R and precision P and in some cases fallout F ) lack universal comparability in the sense that their values depend on the generality of the IR problem. A solution is given by using all "parts" of the database, including the non-relevant documents and also the not-retrieved documents. It turns out that the solution is given by introducing the measure M being the fraction of the not-retrieved documents that are relevant (hence the "miss" measure). We prove that - independent of the IR problem or of the IR action - the quadruple (P,R,F,M) belongs to a universal IR surface, being the same for all IR-activities. This universality is then exploited by defining a new measure for evaluation in IR allowing for unbiased comparisons of all IR results. We also show that only using one, two or even three measures from the set {P,R,F,M} necessary leads to evaluation measures that are non-universal and hence not capable of comparing different IR situations.
- Date
- 14. 8.2004 19:17:22
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Egghe, L.: Untangling Herdan's law and Heaps' law : mathematical and informetric arguments (2007)
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- Abstract
- Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.
- Date
- 29. 4.2007 19:51:08
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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
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Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012)
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- Date
- 14. 2.2012 12:53:22
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Egghe, L.: Type/Token-Taken informetrics (2003)
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- Abstract
- Type/Token-Taken informetrics is a new part of informetrics that studies the use of items rather than the items itself. Here, items are the objects that are produced by the sources (e.g., journals producing articles, authors producing papers, etc.). In linguistics a source is also called a type (e.g., a word), and an item a token (e.g., the use of words in texts). In informetrics, types that occur often, for example, in a database will also be requested often, for example, in information retrieval. The relative use of these occurrences will be higher than their relative occurrences itself; hence, the name Type/ Token-Taken informetrics. This article studies the frequency distribution of Type/Token-Taken informetrics, starting from the one of Type/Token informetrics (i.e., source-item relationships). We are also studying the average number my* of item uses in Type/Token-Taken informetrics and compare this with the classical average number my in Type/Token informetrics. We show that my* >= my always, and that my* is an increasing function of my. A method is presented to actually calculate my* from my, and a given a, which is the exponent in Lotka's frequency distribution of Type/Token informetrics. We leave open the problem of developing non-Lotkaian Type/TokenTaken informetrics.
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Egghe, L.; Rousseau, R.: Introduction to informetrics : quantitative methods in library, documentation and information science (1990)
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- Date
- 29. 2.2008 19:02:46
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Egghe, L.: Influence of adding or deleting items and sources on the h-index (2010)
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- Date
- 31. 5.2010 15:02:29
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Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996)
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- Source
- Journal of information science. 22(1996) no.3, S.165-170
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Egghe, L.: Properties of the n-overlap vector and n-overlap similarity theory (2006)
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- Date
- 3. 1.2007 14:26:29
