Search (7 results, page 1 of 1)

0.0030255679 = product of:
  0.0060511357 = sum of:
    0.0060511357 = product of:
      0.012102271 = sum of:
        0.012102271 = weight(_text_:a in 4859) [ClassicSimilarity], result of:
          0.012102271 = score(doc=4859,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22789092 = fieldWeight in 4859, 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=4859)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper has a dual character dictated by its twofold purpose. First, it is a speculative historiographic essay containing an attempt to fix the present posotion of library and information science within the context of the probabilisitc revolution that has been encompassing all of science. Second, it comprises a guide to practitioners engaged in statistical research in library and information science

Type

a

0.00270615 = product of:
  0.0054123 = sum of:
    0.0054123 = product of:
      0.0108246 = sum of:
        0.0108246 = weight(_text_:a in 4680) [ClassicSimilarity], result of:
          0.0108246 = score(doc=4680,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.20383182 = fieldWeight in 4680, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.125 = fieldNorm(doc=4680)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0021393995 = product of:
  0.004278799 = sum of:
    0.004278799 = product of:
      0.008557598 = sum of:
        0.008557598 = weight(_text_:a in 6026) [ClassicSimilarity], result of:
          0.008557598 = score(doc=6026,freq=20.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16114321 = fieldWeight in 6026, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=6026)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The British controversy over the validity of Urquhart's and Garfield's Laws during the 1970s constitutes an important episode in the formulation of the probability structure of human knowledge. This controversy took place within the historical context of the convergence of two scientific revolutions-the bibliometric and the biometric-that had been launched in Britain. The preceding decades had witnessed major breakthroughs in understanding the probability distributions underlying the use of human knowledge. Two of the most important of these breakthroughs were the laws posited by Donald J. Urquhart and Eugene Garfield, who played major roles in establishing the institutional bases of the bibliometric revolution. For his part, Urquhart began his realization of S. C. Bradford's concept of a national science library by analyzing the borrowing of journals on interlibrary loan from the Science Museum Library in 1956. He found that 10% of the journals accounted for 80% of the loans and formulated Urquhart's Law, by which the interlibrary use of a journal is a measure of its total use. This law underlay the operations of the National Lending Library for Science and Technology (NLLST), which Urquhart founded. The NLLST became the British Library Lending Division (BLLD) and ultimately the British Library Document Supply Centre (BLDSC). In contrast, Garfield did a study of 1969 journal citations as part of the process of creating the Science Citation Index (SCI), formulating his Law of Concentration, by which the bulk of the information needs in science can be satisfied by a relatively small, multidisciplinary core of journals. This law became the operational principle of the Institute for Scientif ic Information created by Garfield. A study at the BLLD under Urquhart's successor, Maurice B. Line, found low correlations of NLLST use with SCI citations, and publication of this study started a major controversy, during which both laws were called into question. The study was based on the faulty use of the Spearman rank correlation coefficient, and the controversy over it was instrumental in causing B. C. Brookes to investigate bibliometric laws as probabilistic phenomena and begin to link the bibliometric with the biometric revolution. This paper concludes with a resolution of the controversy by means of a statistical technique that incorporates Brookes' criticism of the Spearman rank-correlation method and demonstrates the mutual supportiveness of the two laws

Type

a

0.0020506454 = product of:
  0.004101291 = sum of:
    0.004101291 = product of:
      0.008202582 = sum of:
        0.008202582 = weight(_text_:a in 3698) [ClassicSimilarity], result of:
          0.008202582 = score(doc=3698,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1544581 = fieldWeight in 3698, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3698)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This brief communication analyzes the statistics and methods Lotka used to derive his inverse square law of scientific productivity from the standpoint of modern theory. It finds that he violated the norms of this theory by extremely truncating his data on the right. It also proves that Lotka himself played an important role in establishing the commonly used method of identifying power-law behavior by the R2 fit to a regression line on a log-log plot that modern theory considers unreliable by basing the derivation of his law on this very method.

Type

a

0.0017899501 = product of:
  0.0035799001 = sum of:
    0.0035799001 = product of:
      0.0071598003 = sum of:
        0.0071598003 = weight(_text_:a in 3595) [ClassicSimilarity], result of:
          0.0071598003 = score(doc=3595,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13482209 = fieldWeight in 3595, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=3595)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper analyzes the applicability of the article mean citation rate measures in the Science Citation Index Journal Citation Reports (SCI JCR) to the five JCR mathematical subject categories. These measures are the traditional 2-year impact factor as well as the recently added 5-year impact factor and 5-year article influence score. Utilizing the 2008 SCI JCR, the paper compares the probability distributions of the measures in the mathematical categories to the probability distribution of a scientific model of impact factor distribution. The scientific model distribution is highly skewed, conforming to the negative binomial type, with much of the variance due to the important role of review articles in science. In contrast, the three article mean citation rate measures' distributions in the mathematical categories conformed to either the binomial or Poisson, indicating a high degree of randomness. Seeking reasons for this, the paper analyzes the bibliometric structure of Mathematics, finding it a disjointed discipline of isolated subfields with a weak central core of journals, reduced review function, and long cited half-life placing most citations beyond the measures' time limits. These combine to reduce the measures' variance to one commensurate with random error. However, the measures were found capable of identifying important journals. Using data from surveys of the Louisiana State University (LSU) faculty, the paper finds a higher level of consensus among mathematicians and others on which are the important mathematics journals than the measures indicate, positing that much of the apparent randomness may be due to the measures' inapplicability to mathematical disciplines. Moreover, tests of the stability of impact factor ranks across a 5-year time span suggested that the proper model for Mathematics is the negative binomial.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1953) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1953,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1953, 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=1953)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper examines the probability structure of the 2005 Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) Journal Citation Reports (JCR) by analyzing the Impact Factor distributions of their journals. The distribution of the SCI journals corresponded with a distribution generally modeled by the negative binomial distribution, whereas the SSCI distribution fit the Poisson distribution modeling random, rare events. Both Impact Factor distributions were positively skewed - the SCI much more so than the SSCI - indicating excess variance. One of the causes of this excess variance was that the journals highest in the Impact Factor in both JCRs tended to class in subject categories well funded by the National Institutes of Health. The main reason for the SCI Impact Factor distribution being more skewed than the SSCI one was that review journals defining disciplinary paradigms play a much more important role in the sciences than in the social sciences.

Type

a

0.0010148063 = product of:
  0.0020296127 = sum of:
    0.0020296127 = product of:
      0.0040592253 = sum of:
        0.0040592253 = weight(_text_:a in 2840) [ClassicSimilarity], result of:
          0.0040592253 = score(doc=2840,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.07643694 = fieldWeight in 2840, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2840)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a