Search (219 results, page 2 of 11)

0.021425933 = product of:
  0.042851865 = sum of:
    0.042851865 = product of:
      0.08570373 = sum of:
        0.08570373 = weight(_text_:i in 1691) [ClassicSimilarity], result of:
          0.08570373 = score(doc=1691,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.50006545 = fieldWeight in 1691, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.09375 = fieldNorm(doc=1691)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.020200564 = product of:
  0.040401127 = sum of:
    0.040401127 = product of:
      0.080802254 = sum of:
        0.080802254 = weight(_text_:i in 1113) [ClassicSimilarity], result of:
          0.080802254 = score(doc=1113,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.4714662 = fieldWeight in 1113, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0625 = fieldNorm(doc=1113)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Using a very small sample of 8 data sets it was recently shown by De Visscher (2011) that the g-index is very close to the square root of the total number of citations. It was argued that there is no bibliometrically meaningful difference. Using another somewhat larger empirical sample of 26 data sets I show that the difference may be larger and I argue in favor of the g-index.

0.018555403 = product of:
  0.037110806 = sum of:
    0.037110806 = product of:
      0.07422161 = sum of:
        0.07422161 = weight(_text_:i in 1539) [ClassicSimilarity], result of:
          0.07422161 = score(doc=1539,freq=6.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.43306938 = fieldWeight in 1539, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=1539)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper provides a better understanding of the implications of researchers' social networks in bibliographic references. Using a set of chemistry papers and conducting interviews with their authors (n = 32), I characterize the type of relation the author has with the authors of the references contained in his/her paper (n = 3,623). I show that citation relationships do not always involve underlying personal exchanges and that unknown references are an essential component, revealing segmentations in scientific groups. The relationships implied by references are of various strengths and origins. Several inclusive social circles are then identified: co-authors, close acquaintances, colleagues, invisible colleges, peers, contactables, and strangers. I conclude that publication is a device that contributes to a relatively stable distribution among the various social circles that structure scientific sociability.

0.018555403 = product of:
  0.037110806 = sum of:
    0.037110806 = product of:
      0.07422161 = sum of:
        0.07422161 = weight(_text_:i in 2776) [ClassicSimilarity], result of:
          0.07422161 = score(doc=2776,freq=6.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.43306938 = fieldWeight in 2776, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=2776)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article I investigate the shortcomings of exact string match-based author self-citation detection methods. The contributions of this study are twofold. First, I apply a fuzzy string matching algorithm for self-citation detection and benchmark this approach and other common methods of exclusively author name-based self-citation detection against a manually curated ground truth sample. Near full recall can be achieved with the proposed method while incurring only negligible precision loss. Second, I report some important observations from the results about the extent of latent self-citations and their characteristics and give an example of the effect of improved self-citation detection on the document level self-citation rate of real data.

0.018469224 = product of:
  0.036938448 = sum of:
    0.036938448 = product of:
      0.073876895 = sum of:
        0.073876895 = weight(_text_:22 in 4890) [ClassicSimilarity], result of:
          0.073876895 = score(doc=4890,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.46428138 = fieldWeight in 4890, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=4890)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 1.2007 17:02:22

0.018469224 = product of:
  0.036938448 = sum of:
    0.036938448 = product of:
      0.073876895 = sum of:
        0.073876895 = weight(_text_:22 in 1239) [ClassicSimilarity], result of:
          0.073876895 = score(doc=1239,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.46428138 = fieldWeight in 1239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=1239)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

18. 3.2014 19:13:22

0.017854942 = product of:
  0.035709884 = sum of:
    0.035709884 = product of:
      0.07141977 = sum of:
        0.07141977 = weight(_text_:i in 3340) [ClassicSimilarity], result of:
          0.07141977 = score(doc=3340,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41672117 = fieldWeight in 3340, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.078125 = fieldNorm(doc=3340)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

I studied the distribution of articles and citations in journals between 1998 and 2007 according to an empirical function with two exponents. These variables showed good fit to a beta function with two exponents.

0.017675493 = product of:
  0.035350986 = sum of:
    0.035350986 = product of:
      0.07070197 = sum of:
        0.07070197 = weight(_text_:i in 1075) [ClassicSimilarity], result of:
          0.07070197 = score(doc=1075,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41253293 = fieldWeight in 1075, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1075)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.017675493 = product of:
  0.035350986 = sum of:
    0.035350986 = product of:
      0.07070197 = sum of:
        0.07070197 = weight(_text_:i in 2566) [ClassicSimilarity], result of:
          0.07070197 = score(doc=2566,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41253293 = fieldWeight in 2566, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2566)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We present a bibliometric study of a corpus of 839 bibliographic references about automatic indexing, covering the period 1956-2000. We analyse the distribution of authors and works, the obsolescence and its dispersion, and the distribution of the literature by topic, year, and source type. We conclude that: (i) there has been a constant interest on the part of researchers; (ii) the most studied topics were the techniques and methods employed and the general aspects of automatic indexing; (iii) the productivity of the authors does fit a Lotka distribution (Dmax=0.02 and critical value=0.054); (iv) the annual aging factor is 95%; and (v) the dispersion of the literature is low.

0.017675493 = product of:
  0.035350986 = sum of:
    0.035350986 = product of:
      0.07070197 = sum of:
        0.07070197 = weight(_text_:i in 3089) [ClassicSimilarity], result of:
          0.07070197 = score(doc=3089,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41253293 = fieldWeight in 3089, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3089)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article, I investigate the reliability, in the social science sense, of collecting informetric data about the World Wide Web by Web crawling. The investigation includes a critical examination of the practice of Web crawling and contrasts the results of content crawling with the results of link crawling. It is shown that Web crawling by search engines is intentionally biased and selective. I also report the results of a [arge-scale experimental simulation of Web crawling that illustrates the effects of different crawling policies an data collection. It is concluded that the reliability of Web crawling as a data collection technique is improved by fuller reporting of relevant crawling policies.

0.017675493 = product of:
  0.035350986 = sum of:
    0.035350986 = product of:
      0.07070197 = sum of:
        0.07070197 = weight(_text_:i in 3108) [ClassicSimilarity], result of:
          0.07070197 = score(doc=3108,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41253293 = fieldWeight in 3108, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3108)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article uses an evidence-based approach to assess the difficulties faced by developing country scientists in accessing scientific literature. I compare the backward citation patterns of Swiss and Indian scientists in a database of 43,150 scientific papers published by scientists from either country in 2007. Controlling for fields and quality with citing journal fixed effects, I find that Indian scientists have shorter reference lists (-6%) and are more likely to cite articles from open access journals (+50%). Moreover, the difference in the length of the reference list is more pronounced in biology and medicine, where circulation of (free) preprints and conference proceedings is non-existent. Informal file-sharing practices among scientists mitigate the effects of access restrictions.

0.017675493 = product of:
  0.035350986 = sum of:
    0.035350986 = product of:
      0.07070197 = sum of:
        0.07070197 = weight(_text_:i in 2264) [ClassicSimilarity], result of:
          0.07070197 = score(doc=2264,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.41253293 = fieldWeight in 2264, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2264)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The h-index was devised to represent a scholar's contributions to his field with respect to the number of publications and citations. It does not, however, take into consideration the scholar's position in the authorship list. I recommend a new supplementary index to score academics, representing the relative contribution to the papers with impact, be reported alongside the h-index. I call this index the AP-index, and it is simply defined as the average position in which an academic appears in authorship lists, on articles that factor in to that academic's h-index.

0.015462836 = product of:
  0.030925673 = sum of:
    0.030925673 = product of:
      0.061851345 = sum of:
        0.061851345 = weight(_text_:i in 2119) [ClassicSimilarity], result of:
          0.061851345 = score(doc=2119,freq=6.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.36089116 = fieldWeight in 2119, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2119)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In their article "Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient," Ahlgren, Jarneving, and Rousseau fault traditional author cocitation analysis (ACA) for using Pearson's r as a measure of similarity between authors because it fails two tests of stability of measurement. The instabilities arise when rs are recalculated after a first coherent group of authors has been augmented by a second coherent group with whom the first has little or no cocitation. However, AJ&R neither cluster nor map their data to demonstrate how fluctuations in rs will mislead the analyst, and the problem they pose is remote from both theory and practice in traditional ACA. By entering their own rs into multidimensional scaling and clustering routines, I show that, despite r's fluctuations, clusters based an it are much the same for the combined groups as for the separate groups. The combined groups when mapped appear as polarized clumps of points in two-dimensional space, confirming that differences between the groups have become much more important than differences within the groups-an accurate portrayal of what has happened to the data. Moreover, r produces clusters and maps very like those based an other coefficients that AJ&R mention as possible replacements, such as a cosine similarity measure or a chi square dissimilarity measure. Thus, r performs well enough for the purposes of ACA. Accordingly, I argue that qualitative information revealing why authors are cocited is more important than the cautions proposed in the AJ&R critique. I include notes an topics such as handling the diagonal in author cocitation matrices, lognormalizing data, and testing r for significance.

0.015462836 = product of:
  0.030925673 = sum of:
    0.030925673 = product of:
      0.061851345 = sum of:
        0.061851345 = weight(_text_:i in 5220) [ClassicSimilarity], result of:
          0.061851345 = score(doc=5220,freq=6.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.36089116 = fieldWeight in 5220, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5220)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Journal Citation Reports provides a classification of journals most heavily cited by a given journal and which most heavily cite that journal, but size variation is not taken into account. Pudovkin and Garfield suggest a procedure for meeting this difficulty. The relatedness of journal i to journal j is determined by the number of citations from journal i to journal j in a given year normalized by the product of the papers published in the j journal in that year times the number of references cited in the i journal in that year. A multiplier of ten to the sixth is suggested to bring the values into an easily perceptible range. While citations received depend upon the overall cumulative number of papers published by a journal, the current year is utilized since that data is available in JCR. Citations to current year papers would be quite low in most fields and thus not included. To produce the final index, the maximum of the A citing B value, and the B citing A value is chosen and used to indicate the closeness of the journals. The procedure is illustrated for the journal Genetics.

0.015462836 = product of:
  0.030925673 = sum of:
    0.030925673 = product of:
      0.061851345 = sum of:
        0.061851345 = weight(_text_:i in 1968) [ClassicSimilarity], result of:
          0.061851345 = score(doc=1968,freq=6.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.36089116 = fieldWeight in 1968, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1968)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers that received at least h citations. To take into account the highly skewed frequency distribution of citations, L. Egghe (2006a) proposed the g-index as an improvement of the h-index. I have worked out 26 practical cases of physicists from the Institute of Physics at Chemnitz University of Technology, and compare the h and g values in this study. It is demonstrated that the g-index discriminates better between different citation patterns. This also can be achieved by evaluating B.H. Jin's (2006) A-index, which reflects the average number of citations in the h-core, and interpreting it in conjunction with the h-index. h and A can be combined into the R-index to measure the h-core's citation intensity. I also have determined the A and R values for the 26 datasets. For a better comparison, I utilize interpolated indices. The correlations between the various indices as well as with the total number of papers and the highest citation counts are discussed. The largest Pearson correlation coefficient is found between g and R. Although the correlation between g and h is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either h or g.

0.015150423 = product of:
  0.030300846 = sum of:
    0.030300846 = product of:
      0.060601693 = sum of:
        0.060601693 = weight(_text_:i in 111) [ClassicSimilarity], result of:
          0.060601693 = score(doc=111,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.35359967 = fieldWeight in 111, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=111)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Co-word analysis is a technique for detecting the knowledge structure of scientific literature and mapping the dynamics in a research field. It is used to count the co-occurrences of term pairs, compute the strength between term pairs, and map the research field by inserting terms and their linkages into a graphical structure according to the strength values. In previous co-word studies, there are two indexes used to measure the strength between term pairs in order to identify the major areas in a research field - the inclusion index (I) and the equivalence index (E). This study will conduct two co-word analysis experiments using the two indexes, respectively, and compare the results from the two experiments. The results show, due to the difference in their computation, index I is more likely to identify general subject areas in a research field while index E is more likely to identify subject areas at more specific levels

0.015150423 = product of:
  0.030300846 = sum of:
    0.030300846 = product of:
      0.060601693 = sum of:
        0.060601693 = weight(_text_:i in 4124) [ClassicSimilarity], result of:
          0.060601693 = score(doc=4124,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.35359967 = fieldWeight in 4124, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=4124)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The variables investment, benefit, and yield were defined to study the influence of journal self-citations on the impact factor. Investment represents the share of journal self-citations that contribute to the impact factor. Benefit is defined as the ratio of journal impact factor including self-citations to journal impact factor without self-citations. Yield is the relationship between benefit and investment. I selected all journals included in 2008 in the Science Citation Index version of Journal Citation Reports. After deleting 482 records for reasons to be explained, I used a final set of 6,138 journals to study the distribution of the variables defined above. The distribution of benefit differed from the distribution of investment and yield. The top 20-ranked journals were not the same for all three variables. The yield of self-citations on the journal impact factor was, in general, very modest.

0.015150423 = product of:
  0.030300846 = sum of:
    0.030300846 = product of:
      0.060601693 = sum of:
        0.060601693 = weight(_text_:i in 1110) [ClassicSimilarity], result of:
          0.060601693 = score(doc=1110,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.35359967 = fieldWeight in 1110, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=1110)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Using data from the Web of Science (WoS), we analyze the mutual information among university, industry, and government addresses (U-I-G) at the country level for a number of countries. The dynamic evolution of the Triple Helix can thus be compared among developed and developing nations in terms of cross-sectional coauthorship relations. The results show that the Triple Helix interactions among the three subsystems U-I-G become less intensive over time, but unequally for different countries. We suggest that globalization erodes local Triple Helix relations and thus can be expected to have increased differentiation in national systems since the mid-1990s. This effect of globalization is more pronounced in developed countries than in developing ones. In the dynamic analysis, we focus on a more detailed comparison between China and the United States. Specifically, the Chinese Academy of the (Social) Sciences is changing increasingly from a public research institute to an academic one, and this has a measurable effect on China's position in the globalization.

0.015150423 = product of:
  0.030300846 = sum of:
    0.030300846 = product of:
      0.060601693 = sum of:
        0.060601693 = weight(_text_:i in 2906) [ClassicSimilarity], result of:
          0.060601693 = score(doc=2906,freq=4.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.35359967 = fieldWeight in 2906, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=2906)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

0.014283955 = product of:
  0.02856791 = sum of:
    0.02856791 = product of:
      0.05713582 = sum of:
        0.05713582 = weight(_text_:i in 5075) [ClassicSimilarity], result of:
          0.05713582 = score(doc=5075,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.33337694 = fieldWeight in 5075, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0625 = fieldNorm(doc=5075)
      0.5 = coord(1/2)
  0.5 = coord(1/2)