Search (2 results, page 1 of 1)

0.013257908 = product of:
  0.026515815 = sum of:
    0.026515815 = product of:
      0.05303163 = sum of:
        0.05303163 = weight(_text_:h in 1878) [ClassicSimilarity], result of:
          0.05303163 = score(doc=1878,freq=16.0), product of:
            0.113842286 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.045821942 = queryNorm
            0.4658342 = fieldWeight in 1878, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.046875 = fieldNorm(doc=1878)
      0.5 = coord(1/2)
  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.

Object

h-index

0.012352326 = product of:
  0.024704652 = sum of:
    0.024704652 = product of:
      0.049409304 = sum of:
        0.049409304 = weight(_text_:h in 2009) [ClassicSimilarity], result of:
          0.049409304 = score(doc=2009,freq=20.0), product of:
            0.113842286 = queryWeight, product of:
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.045821942 = queryNorm
            0.4340154 = fieldWeight in 2009, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              2.4844491 = idf(docFreq=10020, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2009)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The article studies the influence of the query formulation of a topic on its h-index. In order to generate pure random sets of documents, we used N-grams (N variable) to measure this influence: strings of zeros, truncated at the end. The used databases are WoS and Scopus. The formula h=T**1/alpha, proved in Egghe and Rousseau (2006) where T is the number of retrieved documents and is Lotka's exponent, is confirmed being a concavely increasing function of T. We also give a formula for the relation between h and N the length of the N-gram: h=D10**(-N/alpha) where D is a constant, a convexly decreasing function, which is found in our experiments. Nonlinear regression on h=T**1/alpha gives an estimation of , which can then be used to estimate the h-index of the entire database (Web of Science [WoS] and Scopus): h=S**1/alpha, , where S is the total number of documents in the database.

Object

h-index