Search (27 results, page 1 of 2)

0.032092348 = product of:
  0.16046174 = sum of:
    0.16046174 = weight(_text_:index in 695) [ClassicSimilarity], result of:
      0.16046174 = score(doc=695,freq=10.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.86365044 = fieldWeight in 695, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=695)
  0.2 = coord(1/5)

Abstract

An h-type index is proposed which depends on the obtained citations of articles belonging to the h-core. This weighted h-index, denoted as hw, is presented in a continuous setting and in a discrete one. It is shown that in a continuous setting the new index enjoys many good properties. In the discrete setting some small deviations from the ideal may occur.

Object

h-index

0.03076098 = product of:
  0.1538049 = sum of:
    0.1538049 = weight(_text_:index in 243) [ClassicSimilarity], result of:
      0.1538049 = score(doc=243,freq=12.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.82782143 = fieldWeight in 243, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0546875 = fieldNorm(doc=243)
  0.2 = coord(1/5)

Abstract

Based on earlier results about the shifted Lotka function, we prove an implicit functional relation between the Hirsch index (h-index) and the total number of sources (T). It is shown that the corresponding function, h(T), is concavely increasing. Next, we construct an implicit relation between the h-index and the impact factor IF (an average number of items per source). The corresponding function h(IF) is increasing and we show that if the parameter C in the numerator of the shifted Lotka function is high, then the relation between the h-index and the impact factor is almost linear.

Object

h-index

0.030445471 = product of:
  0.15222736 = sum of:
    0.15222736 = weight(_text_:index in 1864) [ClassicSimilarity], result of:
      0.15222736 = score(doc=1864,freq=4.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.8193307 = fieldWeight in 1864, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.09375 = fieldNorm(doc=1864)
  0.2 = coord(1/5)

Object

g-index

0.028479135 = product of:
  0.14239568 = sum of:
    0.14239568 = weight(_text_:index in 2638) [ClassicSimilarity], result of:
      0.14239568 = score(doc=2638,freq=14.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.7664138 = fieldWeight in 2638, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.046875 = fieldNorm(doc=2638)
  0.2 = coord(1/5)

Abstract

Hirsch-type indices are studied with special attention to the AR**2-index introduced by Jin. The article consists of two parts: a theoretical part and a practical illustration. In the theoretical part, we recall the definition of the AR**2-index and show that an alternative definition, the so-called AR**2,1, does not have the properties expected for this type of index. A practical example shows the existence of some of these mathematical properties and illustrates the difference between different h-type indices. Clearly the h-index itself is the most robust of all. It is shown that excluding so-called non-WoS source articles may have a significant influence on the R-and, especially, the g-index.

Object

h-index

0.025116233 = product of:
  0.12558116 = sum of:
    0.12558116 = weight(_text_:index in 2717) [ClassicSimilarity], result of:
      0.12558116 = score(doc=2717,freq=8.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.67591333 = fieldWeight in 2717, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2717)
  0.2 = coord(1/5)

Abstract

Temporal growth of the h-index in a diachronous cumulative time series is predicted to be linear by Hirsch (2005), whereas other models predict a concave increase. Actual data generally yield a linear growth or S-shaped growth. We study the h-index's growth in computer simulations of the publication-citation process. In most simulations the h-index grows linearly in time. Only occasionally does an S-shape occur, while in our simulations a concave increase is very rare. The latter is often signalled by the occurrence of plateaus - periods of h-index stagnation. Several parameters and their influence on the h-index's growth are determined and discussed.

0.024858627 = product of:
  0.12429313 = sum of:
    0.12429313 = weight(_text_:index in 6759) [ClassicSimilarity], result of:
      0.12429313 = score(doc=6759,freq=6.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.6689808 = fieldWeight in 6759, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=6759)
  0.2 = coord(1/5)

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.

Object

h-index

0.024858627 = product of:
  0.12429313 = sum of:
    0.12429313 = weight(_text_:index in 2351) [ClassicSimilarity], result of:
      0.12429313 = score(doc=2351,freq=6.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.6689808 = fieldWeight in 2351, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=2351)
  0.2 = coord(1/5)

Abstract

A time-dependent h-type indicator is proposed. This indicator depends on the size of the h-core, the number of citations received, and recent change in the value of the h-index. As such, it tries to combine in a dynamic way older information about the source (e.g., a scientist or research institute that is evaluated) with recent information.

Object

h-index

0.02397574 = product of:
  0.059939347 = sum of:
    0.04841807 = weight(_text_:context in 7659) [ClassicSimilarity], result of:
      0.04841807 = score(doc=7659,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.27475408 = fieldWeight in 7659, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.046875 = fieldNorm(doc=7659)
    0.011521274 = product of:
      0.03456382 = sum of:
        0.03456382 = weight(_text_:22 in 7659) [ClassicSimilarity], result of:
          0.03456382 = score(doc=7659,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = 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)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)

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

0.015792316 = product of:
  0.039480787 = sum of:
    0.027959513 = weight(_text_:system in 5270) [ClassicSimilarity], result of:
      0.027959513 = score(doc=5270,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.20878783 = fieldWeight in 5270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=5270)
    0.011521274 = product of:
      0.03456382 = sum of:
        0.03456382 = weight(_text_:22 in 5270) [ClassicSimilarity], result of:
          0.03456382 = score(doc=5270,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.23214069 = fieldWeight in 5270, 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=5270)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)

Abstract

The age distribution of a country's scientists is an important element in the study of its research capacity. In this article we investigate the age distribution of Japanese scientists in order to find out whether major events such as World War II had an appreciable effect on its features. Data have been obtained from population censuses taken in Japan from 1970 to 1995. A comparison with the situation in China and the United States has been made. We find that the group of scientific researchers outside academia is dominated by the young: those younger than age 35. The personnel group in higher education, on the other hand, is dominated by the baby boomers: those who were born after World War II. Contrary to the Chinese situation we could not find any influence of major nondemographic events. The only influence we found was the increase in enrollment of university students after World War II caused by the reform of the Japanese university system. Female participation in the scientific and university systems in Japan, though still low, is increasing.

Date

22. 7.2006 15:26:24

0.015222736 = product of:
  0.07611368 = sum of:
    0.07611368 = weight(_text_:index in 4994) [ClassicSimilarity], result of:
      0.07611368 = score(doc=4994,freq=4.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.40966535 = fieldWeight in 4994, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.046875 = fieldNorm(doc=4994)
  0.2 = coord(1/5)

Abstract

Contrary to what one might expect, Nobel laureates and Fields medalists have a rather large fraction (10% or more) of uncited publications. This is the case for (in total) 75 examined researchers from the fields of mathematics (Fields medalists), physics, chemistry, and physiology or medicine (Nobel laureates). We study several indicators for these researchers, including the h-index, total number of publications, average number of citations per publication, the number (and fraction) of uncited publications, and their interrelations. The most remarkable result is a positive correlation between the h-index and the number of uncited articles. We also present a Lotkaian model, which partially explains the empirically found regularities.

0.011183805 = product of:
  0.055919025 = sum of:
    0.055919025 = weight(_text_:system in 3007) [ClassicSimilarity], result of:
      0.055919025 = score(doc=3007,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.41757566 = fieldWeight in 3007, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.09375 = fieldNorm(doc=3007)
  0.2 = coord(1/5)

0.009683615 = product of:
  0.04841807 = sum of:
    0.04841807 = weight(_text_:context in 5205) [ClassicSimilarity], result of:
      0.04841807 = score(doc=5205,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.27475408 = fieldWeight in 5205, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.046875 = fieldNorm(doc=5205)
  0.2 = coord(1/5)

0.009683615 = product of:
  0.04841807 = sum of:
    0.04841807 = weight(_text_:context in 250) [ClassicSimilarity], result of:
      0.04841807 = score(doc=250,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.27475408 = fieldWeight in 250, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.046875 = fieldNorm(doc=250)
  0.2 = coord(1/5)

Abstract

In this article, we report on a discrete choice experiment to determine the willingness-to-wait (WTW) in the context of journal submissions. Respondents to our survey are mostly active in the information sciences, including librarians. Besides WTW, other attributes included in the study are the quality of the editorial board, the quality of referee reports, the probability of being accepted, the ISI impact factor, and the standing of the journal among peers. Interaction effects originating from scientists' personal characteristics (age, region of origin, motivations to publish) with the WTW are highlighted. A difference was made between submitting a high quality article and a standard article. Among the interesting results obtained from our analysis we mention that for a high-quality article, researchers are willing to wait some 18 months longer for a journal with an ISI impact factor above 2 than for a journal without an impact factor, keeping all other factors constant. For a standard article, the WTW decreases to some 8 months. Gender had no effect on our conclusions.

0.008970084 = product of:
  0.044850416 = sum of:
    0.044850416 = weight(_text_:index in 5157) [ClassicSimilarity], result of:
      0.044850416 = score(doc=5157,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.24139762 = fieldWeight in 5157, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5157)
  0.2 = coord(1/5)

Abstract

Measurement of the degree of interconnectedness in graph like networks of hyperlinks or citations can indicate the existence of research fields and assist in comparative evaluation of research efforts. In this issue we begin with Egghe and Rousseau who review compactness measures and investigate the compactness of a network as a weighted graph with dissimilarity values characterizing the arcs between nodes. They make use of a generalization of the Botofogo, Rivlin, Shneiderman, (BRS) compaction measure which treats the distance between unreachable nodes not as infinity but rather as the number of nodes in the network. The dissimilarity values are determined by summing the reciprocals of the weights of the arcs in the shortest chain between two nodes where no weight is smaller than one. The BRS measure is then the maximum value for the sum of the dissimilarity measures less the actual sum divided by the difference between the maximum and minimum. The Wiener index, the sum of all elements in the dissimilarity matrix divided by two, is then computed for Small's particle physics co-citation data as well as the BRS measure, the dissimilarity values and shortest paths. The compactness measure for the weighted network is smaller than for the un-weighted. When the bibliographic coupling network is utilized it is shown to be less compact than the co-citation network which indicates that the new measure produces results that confirm to an obvious case.

0.008069678 = product of:
  0.040348392 = sum of:
    0.040348392 = weight(_text_:context in 5226) [ClassicSimilarity], result of:
      0.040348392 = score(doc=5226,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.22896172 = fieldWeight in 5226, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5226)
  0.2 = coord(1/5)

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

0.007908144 = product of:
  0.03954072 = sum of:
    0.03954072 = weight(_text_:system in 4384) [ClassicSimilarity], result of:
      0.03954072 = score(doc=4384,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.29527056 = fieldWeight in 4384, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=4384)
  0.2 = coord(1/5)

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

0.006523886 = product of:
  0.03261943 = sum of:
    0.03261943 = weight(_text_:system in 2157) [ClassicSimilarity], result of:
      0.03261943 = score(doc=2157,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 2157, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2157)
  0.2 = coord(1/5)

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

0.0038404248 = product of:
  0.019202124 = sum of:
    0.019202124 = product of:
      0.057606373 = sum of:
        0.057606373 = weight(_text_:22 in 4992) [ClassicSimilarity], result of:
          0.057606373 = score(doc=4992,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = 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)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)

Date

14. 2.2012 12:53:22

0.0031002287 = product of:
  0.015501143 = sum of:
    0.015501143 = product of:
      0.04650343 = sum of:
        0.04650343 = weight(_text_:29 in 1746) [ClassicSimilarity], result of:
          0.04650343 = score(doc=1746,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.31092256 = fieldWeight in 1746, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=1746)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)

Date

18. 8.2006 14:29:40

0.0031002287 = product of:
  0.015501143 = sum of:
    0.015501143 = product of:
      0.04650343 = sum of:
        0.04650343 = weight(_text_:29 in 1197) [ClassicSimilarity], result of:
          0.04650343 = score(doc=1197,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.31092256 = fieldWeight in 1197, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=1197)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)

Date

29. 1.2014 16:31:35