Search (59 results, page 1 of 3)

  • × author_ss:"Egghe, L."
  1. Egghe, L.; Ravichandra Rao, I.K.: Study of different h-indices for groups of authors (2008) 0.05 
    0.048801746 = sum of:
  0.04475294 = product of:
    0.17901176 = sum of:
      0.17901176 = weight(_text_:authors in 1878) [ClassicSimilarity], result of:
        0.17901176 = score(doc=1878,freq=12.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.7402591 = fieldWeight in 1878, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=1878)
    0.25 = coord(1/4)
  0.004048805 = product of:
    0.00809761 = sum of:
      0.00809761 = weight(_text_:a in 1878) [ClassicSimilarity], result of:
        0.00809761 = score(doc=1878,freq=6.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.13239266 = fieldWeight in 1878, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=1878)
    0.5 = coord(1/2)
    Abstract
    In this article, for any group of authors, we define three different h-indices. First, there is the successive h-index h2 based on the ranked list of authors and their h-indices h1 as defined by Schubert (2007). Next, there is the h-index hP based on the ranked list of authors and their number of publications. Finally, there is the h-index hC based on the ranked list of authors and their number of citations. We present formulae for these three indices in Lotkaian informetrics from which it also follows that h2 < hp < hc. We give a concrete example of a group of 167 authors on the topic optical flow estimation. Besides these three h-indices, we also calculate the two-by-two Spearman rank correlation coefficient and prove that these rankings are significantly related.
    Type
    a
  2. Egghe, L.; Bornmann, L.: Fallout and miss in journal peer review (2013) 0.04 
    0.040776104 = sum of:
  0.036919296 = product of:
    0.14767718 = sum of:
      0.14767718 = weight(_text_:authors in 1759) [ClassicSimilarity], result of:
        0.14767718 = score(doc=1759,freq=6.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.61068267 = fieldWeight in 1759, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1759)
    0.25 = coord(1/4)
  0.0038568082 = product of:
    0.0077136164 = sum of:
      0.0077136164 = weight(_text_:a in 1759) [ClassicSimilarity], result of:
        0.0077136164 = score(doc=1759,freq=4.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.12611452 = fieldWeight in 1759, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1759)
    0.5 = coord(1/2)
    Abstract
    Purpose - The authors exploit the analogy between journal peer review and information retrieval in order to quantify some imperfections of journal peer review. Design/methodology/approach - The authors define fallout rate and missing rate in order to describe quantitatively the weak papers that were accepted and the strong papers that were missed, respectively. To assess the quality of manuscripts the authors use bibliometric measures. Findings - Fallout rate and missing rate are put in relation with the hitting rate and success rate. Conclusions are drawn on what fraction of weak papers will be accepted in order to have a certain fraction of strong accepted papers. Originality/value - The paper illustrates that these curves are new in peer review research when interpreted in the information retrieval terminology.
    Type
    a
  3. Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012) 0.04 
    0.039830416 = product of:
  0.07966083 = sum of:
    0.07966083 = sum of:
      0.0077919285 = weight(_text_:a in 4992) [ClassicSimilarity], result of:
        0.0077919285 = score(doc=4992,freq=2.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.12739488 = fieldWeight in 4992, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.078125 = fieldNorm(doc=4992)
      0.071868904 = weight(_text_:22 in 4992) [ClassicSimilarity], result of:
        0.071868904 = score(doc=4992,freq=2.0), product of:
          0.1857552 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.053045183 = queryNorm
          0.38690117 = fieldWeight in 4992, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=4992)
  0.5 = coord(1/2)
    Date
    14. 2.2012 12:53:22
    Type
    a
  4. Egghe, L.; Ravichandra Rao, I.K.: Duality revisited : construction of fractional frequency distributions based on two dual Lotka laws (2002) 0.04 
    0.036872096 = sum of:
  0.03164511 = product of:
    0.12658045 = sum of:
      0.12658045 = weight(_text_:authors in 1006) [ClassicSimilarity], result of:
        0.12658045 = score(doc=1006,freq=6.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.52344227 = fieldWeight in 1006, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=1006)
    0.25 = coord(1/4)
  0.005226985 = product of:
    0.01045397 = sum of:
      0.01045397 = weight(_text_:a in 1006) [ClassicSimilarity], result of:
        0.01045397 = score(doc=1006,freq=10.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.1709182 = fieldWeight in 1006, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=1006)
    0.5 = coord(1/2)
    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.
    Type
    a
  5. Egghe, L.: ¬A model for the size-frequency function of coauthor pairs (2008) 0.04 
    0.036872096 = sum of:
  0.03164511 = product of:
    0.12658045 = sum of:
      0.12658045 = weight(_text_:authors in 2366) [ClassicSimilarity], result of:
        0.12658045 = score(doc=2366,freq=6.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.52344227 = fieldWeight in 2366, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=2366)
    0.25 = coord(1/4)
  0.005226985 = product of:
    0.01045397 = sum of:
      0.01045397 = weight(_text_:a in 2366) [ClassicSimilarity], result of:
        0.01045397 = score(doc=2366,freq=10.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.1709182 = fieldWeight in 2366, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=2366)
    0.5 = coord(1/2)
    Abstract
    Lotka's law was formulated to describe the number of authors with a certain number of publications. Empirical results (Morris & Goldstein, 2007) indicate that Lotka's law is also valid if one counts the number of publications of coauthor pairs. This article gives a simple model proving this to be true, with the same Lotka exponent, if the number of coauthored papers is proportional to the number of papers of the individual coauthors. Under the assumption that this number of coauthored papers is more than proportional to the number of papers of the individual authors (to be explained in the article), we can prove that the size-frequency function of coauthor pairs is Lotkaian with an exponent that is higher than that of the Lotka function of individual authors, a fact that is confirmed in experimental results.
    Type
    a
  6. Egghe, L.: Empirical and combinatorial study of country occurrences in multi-authored papers (2006) 0.04 
    0.035666242 = sum of:
  0.029835295 = product of:
    0.11934118 = sum of:
      0.11934118 = weight(_text_:authors in 81) [ClassicSimilarity], result of:
        0.11934118 = score(doc=81,freq=12.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.49350607 = fieldWeight in 81, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.03125 = fieldNorm(doc=81)
    0.25 = coord(1/4)
  0.0058309454 = product of:
    0.011661891 = sum of:
      0.011661891 = weight(_text_:a in 81) [ClassicSimilarity], result of:
        0.011661891 = score(doc=81,freq=28.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.19066721 = fieldWeight in 81, product of:
            5.2915025 = tf(freq=28.0), with freq of:
              28.0 = termFreq=28.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=81)
    0.5 = coord(1/2)
    Abstract
    Papers written by several authors can be classified according to the countries of the author affiliations. The empirical part of this paper consists of two datasets. One dataset consists of 1,035 papers retrieved via the search "pedagog*" in the years 2004 and 2005 (up to October) in Academic Search Elite which is a case where phi(m) = the number of papers with m =1, 2,3 ... authors is decreasing, hence most of the papers have a low number of authors. Here we find that #, m = the number of times a country occurs j times in a m-authored paper, j =1, ..., m-1 is decreasing and that # m, m is much higher than all the other #j, m values. The other dataset consists of 3,271 papers retrieved via the search "enzyme" in the year 2005 (up to October) in the same database which is a case of a non-decreasing phi(m): most papers have 3 or 4 authors and we even find many papers with a much higher number of authors. In this case we show again that # m, m is much higher than the other #j, m values but that #j, m is not decreasing anymore in j =1, ..., m-1, although #1, m is (apart from # m, m) the largest number amongst the #j,m. The combinatorial part gives a proof of the fact that #j,m decreases for j = 1, m-1, supposing that all cases are equally possible. This shows that the first dataset is more conform with this model than the second dataset. Explanations for these findings are given. From the data we also find the (we think: new) distribution of number of papers with n =1, 2,3,... countries (i.e. where there are n different countries involved amongst the m (a n) authors of a paper): a fast decreasing function e.g. as a power law with a very large Lotka exponent.
    Type
    a
  7. Egghe, L.; Rousseau, R.; Hooydonk, G. van: Methods for accrediting publications to authors or countries : consequences for evaluation studies (2000) 0.03 
    0.034950946 = sum of:
  0.03164511 = product of:
    0.12658045 = sum of:
      0.12658045 = weight(_text_:authors in 4384) [ClassicSimilarity], result of:
        0.12658045 = score(doc=4384,freq=6.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.52344227 = fieldWeight in 4384, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=4384)
    0.25 = coord(1/4)
  0.0033058354 = product of:
    0.006611671 = sum of:
      0.006611671 = weight(_text_:a in 4384) [ClassicSimilarity], result of:
        0.006611671 = score(doc=4384,freq=4.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.10809815 = fieldWeight in 4384, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=4384)
    0.5 = coord(1/2)
    Abstract
    One aim of science evaluation studies is to determine quantitatively the contribution of different players (authors, departments, countries) to the whole system. This information is then used to study the evolution of the system, for instance to gauge the results of special national or international programs. Taking articles as our basic data, we want to determine the exact relative contribution of each coauthor or each country. These numbers are brought together to obtain country scores, or department scores, etc. It turns out, as we will show in this article, that different scoring methods can yield totally different rankings. Conseqeuntly, a ranking between countries, universities, research groups or authors, based on one particular accrediting methods does not contain an absolute truth about their relative importance
    Type
    a
  8. Egghe, L.; Rousseau, R.: Averaging and globalising quotients of informetric and scientometric data (1996) 0.03 
    0.028573405 = product of:
  0.05714681 = sum of:
    0.05714681 = sum of:
      0.014025472 = weight(_text_:a in 7659) [ClassicSimilarity], result of:
        0.014025472 = score(doc=7659,freq=18.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.22931081 = fieldWeight in 7659, product of:
            4.2426405 = tf(freq=18.0), with freq of:
              18.0 = termFreq=18.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=7659)
      0.043121338 = weight(_text_:22 in 7659) [ClassicSimilarity], result of:
        0.043121338 = score(doc=7659,freq=2.0), product of:
          0.1857552 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.053045183 = queryNorm
          0.23214069 = fieldWeight in 7659, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=7659)
  0.5 = coord(1/2)
    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
    Type
    a
  9. Egghe, L.: ¬A universal method of information retrieval evaluation : the "missing" link M and the universal IR surface (2004) 0.03 
    0.026787654 = product of:
  0.053575307 = sum of:
    0.053575307 = sum of:
      0.01045397 = weight(_text_:a in 2558) [ClassicSimilarity], result of:
        0.01045397 = score(doc=2558,freq=10.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.1709182 = fieldWeight in 2558, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=2558)
      0.043121338 = weight(_text_:22 in 2558) [ClassicSimilarity], result of:
        0.043121338 = score(doc=2558,freq=2.0), product of:
          0.1857552 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.053045183 = queryNorm
          0.23214069 = fieldWeight in 2558, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2558)
  0.5 = coord(1/2)
    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
    Type
    a
  10. Egghe, L.: Mathematical theory of the h- and g-index in case of fractional counting of authorship (2008) 0.02 
    0.023497296 = sum of:
  0.018270312 = product of:
    0.07308125 = sum of:
      0.07308125 = weight(_text_:authors in 2004) [ClassicSimilarity], result of:
        0.07308125 = score(doc=2004,freq=2.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.30220953 = fieldWeight in 2004, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=2004)
    0.25 = coord(1/4)
  0.005226985 = product of:
    0.01045397 = sum of:
      0.01045397 = weight(_text_:a in 2004) [ClassicSimilarity], result of:
        0.01045397 = score(doc=2004,freq=10.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.1709182 = fieldWeight in 2004, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=2004)
    0.5 = coord(1/2)
    Abstract
    This article studies the h-index (Hirsch index) and the g-index of authors, in case one counts authorship of the cited articles in a fractional way. There are two ways to do this: One counts the citations to these papers in a fractional way or one counts the ranks of the papers in a fractional way as credit for an author. In both cases, we define the fractional h- and g-indexes, and we present inequalities (both upper and lower bounds) between these fractional h- and g-indexes and their corresponding unweighted values (also involving, of course, the coauthorship distribution). Wherever applicable, examples and counterexamples are provided. In a concrete example (the publication citation list of the present author), we make explicit calculations of these fractional h- and g-indexes and show that they are not very different from the unweighted ones.
    Type
    a
  11. Egghe, L.: Relations between the continuous and the discrete Lotka power function (2005) 0.02 
    0.021576148 = sum of:
  0.018270312 = product of:
    0.07308125 = sum of:
      0.07308125 = weight(_text_:authors in 3464) [ClassicSimilarity], result of:
        0.07308125 = score(doc=3464,freq=2.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.30220953 = fieldWeight in 3464, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=3464)
    0.25 = coord(1/4)
  0.0033058354 = product of:
    0.006611671 = sum of:
      0.006611671 = weight(_text_:a in 3464) [ClassicSimilarity], result of:
        0.006611671 = score(doc=3464,freq=4.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.10809815 = fieldWeight in 3464, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=3464)
    0.5 = coord(1/2)
    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.
    Type
    a
  12. Egghe, L.: Type/Token-Taken informetrics (2003) 0.02 
    0.021385321 = sum of:
  0.0152252605 = product of:
    0.060901042 = sum of:
      0.060901042 = weight(_text_:authors in 1608) [ClassicSimilarity], result of:
        0.060901042 = score(doc=1608,freq=2.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.25184128 = fieldWeight in 1608, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1608)
    0.25 = coord(1/4)
  0.0061600604 = product of:
    0.012320121 = sum of:
      0.012320121 = weight(_text_:a in 1608) [ClassicSimilarity], result of:
        0.012320121 = score(doc=1608,freq=20.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.20142901 = fieldWeight in 1608, product of:
            4.472136 = tf(freq=20.0), with freq of:
              20.0 = termFreq=20.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1608)
    0.5 = coord(1/2)
    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.
    Type
    a
  13. Rousseau, R.; Egghe, L.; Guns, R.: Becoming metric-wise : a bibliometric guide for researchers (2018) 0.02 
    0.018599264 = sum of:
  0.0152252605 = product of:
    0.060901042 = sum of:
      0.060901042 = weight(_text_:authors in 5226) [ClassicSimilarity], result of:
        0.060901042 = score(doc=5226,freq=2.0), product of:
          0.24182312 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.053045183 = queryNorm
          0.25184128 = fieldWeight in 5226, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5226)
    0.25 = coord(1/4)
  0.0033740045 = product of:
    0.006748009 = sum of:
      0.006748009 = weight(_text_:a in 5226) [ClassicSimilarity], result of:
        0.006748009 = score(doc=5226,freq=6.0), product of:
          0.06116359 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.053045183 = queryNorm
          0.11032722 = fieldWeight in 5226, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5226)
    0.5 = coord(1/2)
    Abstract
    Aims to inform researchers about metrics so that they become aware of the evaluative techniques being applied to their scientific output. Understanding these concepts will help them during their funding initiatives, and in hiring and tenure. The book not only describes what indicators do (or are designed to do, which is not always the same thing), but also gives precise mathematical formulae so that indicators can be properly understood and evaluated. Metrics have become a critical issue in science, with widespread international discussion taking place on the subject across scientific journals and organizations. As researchers should know the publication-citation context, the mathematical formulae of indicators being used by evaluating committees and their consequences, and how such indicators might be misused, this book provides an ideal tome on the topic. Provides researchers with a detailed understanding of bibliometric indicators and their applications. Empowers researchers looking to understand the indicators relevant to their work and careers. Presents an informed and rounded picture of bibliometrics, including the strengths and shortcomings of particular indicators. Supplies the mathematics behind bibliometric indicators so they can be properly understood. Written by authors with longstanding expertise who are considered global leaders in the field of bibliometrics
  14. Egghe, L.: ¬A good normalized impact and concentration measure (2014) 0.00 
    0.0047715628 = product of:
  0.0095431255 = sum of:
    0.0095431255 = product of:
      0.019086251 = sum of:
        0.019086251 = weight(_text_:a in 1508) [ClassicSimilarity], result of:
          0.019086251 = score(doc=1508,freq=12.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.3120525 = fieldWeight in 1508, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=1508)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    It is shown that a normalized version of the g-index is a good normalized impact and concentration measure. A proposal for such a measure by Bartolucci is improved.
    Type
    a
  15. Egghe, L.: Untangling Herdan's law and Heaps' law : mathematical and informetric arguments (2007) 0.00 
    0.004132294 = product of:
  0.008264588 = sum of:
    0.008264588 = product of:
      0.016529176 = sum of:
        0.016529176 = weight(_text_:a in 271) [ClassicSimilarity], result of:
          0.016529176 = score(doc=271,freq=36.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.27024537 = fieldWeight in 271, product of:
              6.0 = tf(freq=36.0), with freq of:
                36.0 = termFreq=36.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=271)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Type
    a
  16. Egghe, L.: ¬A rationale for the Hirsch-index rank-order distribution and a comparison with the impact factor rank-order distribution (2009) 0.00 
    0.0040907627 = product of:
  0.008181525 = sum of:
    0.008181525 = product of:
      0.01636305 = sum of:
        0.01636305 = weight(_text_:a in 3124) [ClassicSimilarity], result of:
          0.01636305 = score(doc=3124,freq=18.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.26752928 = fieldWeight in 3124, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3124)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    We present a rationale for the Hirsch-index rank-order distribution and prove that it is a power law (hence a straight line in the log-log scale). This is confirmed by experimental data of Pyykkö and by data produced in this article on 206 mathematics journals. This distribution is of a completely different nature than the impact factor (IF) rank-order distribution which (as proved in a previous article) is S-shaped. This is also confirmed by our example. Only in the log-log scale of the h-index distribution do we notice a concave deviation of the straight line for higher ranks. This phenomenon is discussed.
    Type
    a
  17. Egghe, L.: Dynamic h-index : the Hirsch index in function of time (2007) 0.00 
    0.00381725 = product of:
  0.0076345 = sum of:
    0.0076345 = product of:
      0.015269 = sum of:
        0.015269 = weight(_text_:a in 147) [ClassicSimilarity], result of:
          0.015269 = score(doc=147,freq=12.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.24964198 = fieldWeight in 147, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=147)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    When there are a group of articles and the present time is fixed we can determine the unique number h being the number of articles that received h or more citations while the other articles received a number of citations which is not larger than h. In this article, the time dependence of the h-index is determined. This is important to describe the expected career evolution of a scientist's work or of a journal's production in a fixed year.
    Type
    a
  18. Egghe, L.; Rousseau, R.: Topological aspects of information retrieval (1998) 0.00 
    0.0036077136 = product of:
  0.0072154272 = sum of:
    0.0072154272 = product of:
      0.0144308545 = sum of:
        0.0144308545 = weight(_text_:a in 2157) [ClassicSimilarity], result of:
          0.0144308545 = score(doc=2157,freq=14.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.23593865 = fieldWeight in 2157, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2157)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Let (DS, DQ, sim) be a retrieval system consisting of a document space DS, a query space QS, and a function sim, expressing the similarity between a document and a query. Following D.M. Everett and S.C. Cater (1992), we introduce topologies on the document space. These topologies are generated by the similarity function sim and the query space QS. 3 topologies will be studied: the retrieval topology, the similarity topology and the (pseudo-)metric one. It is shown that the retrieval topology is the coarsest of the three, while the (pseudo-)metric is the strongest. These 3 topologies are generally different, reflecting distinct topological aspects of information retrieval. We present necessary and sufficient conditions for these topological aspects to be equal
    Type
    a
  19. Egghe, L.: New relations between similarity measures for vectors based on vector norms (2009) 0.00 
    0.003506368 = product of:
  0.007012736 = sum of:
    0.007012736 = product of:
      0.014025472 = sum of:
        0.014025472 = weight(_text_:a in 2708) [ClassicSimilarity], result of:
          0.014025472 = score(doc=2708,freq=18.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.22931081 = fieldWeight in 2708, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2708)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The well-known similarity measures Jaccard, Salton's cosine, Dice, and several related overlap measures for vectors are compared. While general relations are not possible to prove, we study these measures on the trajectories of the form [X]=a[Y], where a > 0 is a constant and [·] denotes the Euclidean norm of a vector. In this case, direct functional relations between these measures are proved. For Jaccard, we prove that it is a convexly increasing function of Salton's cosine measure, but always smaller than or equal to the latter, hereby explaining a curve, experimentally found by Leydesdorff. All the other measures have a linear relation with Salton's cosine, reducing even to equality, in case a = 1. Hence, for equally normed vectors (e.g., for normalized vectors) we, essentially, only have Jaccard's measure and Salton's cosine measure since all the other measures are equal to the latter.
    Type
    a
  20. Egghe, L.; Liang, L.; Rousseau, R.: ¬A relation between h-index and impact factor in the power-law model (2009) 0.00 
    0.0034846568 = product of:
  0.0069693136 = sum of:
    0.0069693136 = product of:
      0.013938627 = sum of:
        0.013938627 = weight(_text_:a in 6759) [ClassicSimilarity], result of:
          0.013938627 = score(doc=6759,freq=10.0), product of:
            0.06116359 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.053045183 = queryNorm
            0.22789092 = fieldWeight in 6759, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=6759)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Using a power-law model, the two best-known topics in citation analysis, namely the impact factor and the Hirsch index, are unified into one relation (not a function). The validity of our model is, at least in a qualitative way, confirmed by real data.
    Type
    a

Authors

Years

Types

  • a 58
  • m 1
  • More… Less…

Themes

Subjects

Classifications