Search (18 results, page 1 of 1)

0.013177326 = product of:
  0.039531976 = sum of:
    0.039531976 = product of:
      0.059297964 = sum of:
        0.029782942 = weight(_text_:29 in 4463) [ClassicSimilarity], result of:
          0.029782942 = score(doc=4463,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.19432661 = fieldWeight in 4463, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4463)
        0.029515022 = weight(_text_:22 in 4463) [ClassicSimilarity], result of:
          0.029515022 = score(doc=4463,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.19345059 = fieldWeight in 4463, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4463)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Date

29. 9.2018 11:22:09

0.00918649 = product of:
  0.02755947 = sum of:
    0.02755947 = product of:
      0.08267841 = sum of:
        0.08267841 = weight(_text_:network in 1505) [ClassicSimilarity], result of:
          0.08267841 = score(doc=1505,freq=6.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.42611307 = fieldWeight in 1505, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1505)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

International coauthorship relations have increasingly shaped another dynamic in the natural and life sciences during recent decades. However, much less is known about such internationalization in the social sciences. In this study, we analyze international and domestic coauthorship relations of all citable items in the DVD version of the Social Sciences Citation Index 2011 (SSCI). Network statistics indicate 4 groups of nations: (a) an Asian-Pacific one to which all Anglo-Saxon nations (including the United Kingdom and Ireland) are attributed, (b) a continental European one including also the Latin-American countries, (c) the Scandinavian nations, and (d) a community of African nations. Within the EU-28, 11 of the EU-15 states have dominant positions. In many respects, the network parameters are not so different from the Science Citation Index. In addition to these descriptive statistics, we address the question of the relative weights of the international versus domestic networks. An information-theoretical test is proposed at the level of organizational addresses within each nation; the results are mixed, but the international dimension is more important than the national one in the aggregated sets (as in the Science Citation Index). In some countries (e.g., France), however, the national distribution is leading more than the international one. Decomposition of the United States in terms of states shows a similarly mixed result; more U.S. states are domestically oriented in the SSCI and more internationally in the SCI. The international networks have grown during the last decades in addition to the national ones but not by replacing them.

0.00918649 = product of:
  0.02755947 = sum of:
    0.02755947 = product of:
      0.08267841 = sum of:
        0.08267841 = weight(_text_:network in 3090) [ClassicSimilarity], result of:
          0.08267841 = score(doc=3090,freq=6.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.42611307 = fieldWeight in 3090, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3090)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) with similar data based on Scopus 2012. First, global and overlay maps were developed for the 2 sets separately. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the 2 sets: 96.4% of the 10,936 journals contained in JCR, or 51.2% of the 20,554 journals covered by Scopus. Network analysis was pursued on the set of journals shared between the 2 databases and the 2 sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than in Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (i.e., numbers of citing journals) or total citations is similar in both databases overall (Spearman rank correlation ??>?0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important-they are citing shared journals rather than being cited by them-but the humanities are covered better in Scopus than in JCR.

0.009000885 = product of:
  0.027002655 = sum of:
    0.027002655 = product of:
      0.081007965 = sum of:
        0.081007965 = weight(_text_:network in 3704) [ClassicSimilarity], result of:
          0.081007965 = score(doc=3704,freq=4.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.41750383 = fieldWeight in 3704, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=3704)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

0.009000885 = product of:
  0.027002655 = sum of:
    0.027002655 = product of:
      0.081007965 = sum of:
        0.081007965 = weight(_text_:network in 4445) [ClassicSimilarity], result of:
          0.081007965 = score(doc=4445,freq=4.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.41750383 = fieldWeight in 4445, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=4445)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nanocrystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI subject categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time while the network evolves into an oligopolistic control structure with small-world characteristics. The transition from disciplinary-oriented ("Mode 1") to transfer-oriented ("Mode 2") research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC.

0.007500738 = product of:
  0.022502214 = sum of:
    0.022502214 = product of:
      0.06750664 = sum of:
        0.06750664 = weight(_text_:network in 3328) [ClassicSimilarity], result of:
          0.06750664 = score(doc=3328,freq=4.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.34791988 = fieldWeight in 3328, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3328)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Using 3 years of the Journal Citation Reports (2011, 2012, and 2013), indicators of transitions in 2012 (between 2011 and 2013) were studied using methodologies based on entropy statistics. Changes can be indicated at the level of journals using the margin totals of entropy production along the row or column vectors, but also at the level of links among journals by importing the transition matrices into network analysis and visualization programs (and using community-finding algorithms). Seventy-four journals were flagged in terms of discontinuous changes in their citations, but 3,114 journals were involved in "hot" links. Most of these links are embedded in a main component; 78 clusters (containing 172 journals) were flagged as potential "hot spots" emerging at the network level. An additional finding was that PLoS ONE introduced a new communication dynamic into the database. The limitations of the methodology were elaborated using an example. The results of the study indicate where developments in the citation dynamics can be considered as significantly unexpected. This can be used as heuristic information, but what a "hot spot" in terms of the entropy statistics of aggregated citation relations means substantively can be expected to vary from case to case.

0.007425352 = product of:
  0.022276055 = sum of:
    0.022276055 = product of:
      0.06682816 = sum of:
        0.06682816 = weight(_text_:network in 4466) [ClassicSimilarity], result of:
          0.06682816 = score(doc=4466,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.3444231 = fieldWeight in 4466, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4466)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Fractional counting of citations can improve on ranking of multidisciplinary research units (such as universities) by normalizing the differences among fields of science in terms of differences in citation behavior. Furthermore, normalization in terms of citing papers abolishes the unsolved questions in scientometrics about the delineation of fields of science in terms of journals and normalization when comparing among different (sets of) journals. Using publication and citation data of seven Korean research universities, we demonstrate the advantages and the differences in the rankings, explain the possible statistics, and suggest ways to visualize the differences in (citing) audiences in terms of a network.

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 3335) [ClassicSimilarity], result of:
          0.047734402 = score(doc=3335,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 3335, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3335)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Because the Scopus database contains a larger number of journals and covers the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is because of (a) the larger number of journals covered by Scopus and (b) the historical record of citations older than 10 years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 1531) [ClassicSimilarity], result of:
          0.047734402 = score(doc=1531,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 1531, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1531)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 2047) [ClassicSimilarity], result of:
          0.047734402 = score(doc=2047,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 2047, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2047)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The BRICS countries (Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US.

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 3231) [ClassicSimilarity], result of:
          0.047734402 = score(doc=3231,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 3231, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3231)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 3538) [ClassicSimilarity], result of:
          0.047734402 = score(doc=3538,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 3538, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3538)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n?=?687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.

0.0053038225 = product of:
  0.015911467 = sum of:
    0.015911467 = product of:
      0.047734402 = sum of:
        0.047734402 = weight(_text_:network in 5225) [ClassicSimilarity], result of:
          0.047734402 = score(doc=5225,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.2460165 = fieldWeight in 5225, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5225)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.

0.0039353366 = product of:
  0.011806009 = sum of:
    0.011806009 = product of:
      0.035418026 = sum of:
        0.035418026 = weight(_text_:22 in 4681) [ClassicSimilarity], result of:
          0.035418026 = score(doc=4681,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.23214069 = fieldWeight in 4681, 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=4681)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

8. 1.2019 18:22:45

0.003309216 = product of:
  0.009927647 = sum of:
    0.009927647 = product of:
      0.029782942 = sum of:
        0.029782942 = weight(_text_:29 in 1200) [ClassicSimilarity], result of:
          0.029782942 = score(doc=1200,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.19432661 = fieldWeight in 1200, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1200)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

29. 1.2014 16:38:28

0.003309216 = product of:
  0.009927647 = sum of:
    0.009927647 = product of:
      0.029782942 = sum of:
        0.029782942 = weight(_text_:29 in 1211) [ClassicSimilarity], result of:
          0.029782942 = score(doc=1211,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.19432661 = fieldWeight in 1211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1211)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

29. 1.2014 16:44:54

0.003279447 = product of:
  0.009838341 = sum of:
    0.009838341 = product of:
      0.029515022 = sum of:
        0.029515022 = weight(_text_:22 in 4186) [ClassicSimilarity], result of:
          0.029515022 = score(doc=4186,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.19345059 = fieldWeight in 4186, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4186)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

22. 1.2011 12:51:07

0.003279447 = product of:
  0.009838341 = sum of:
    0.009838341 = product of:
      0.029515022 = sum of:
        0.029515022 = weight(_text_:22 in 3089) [ClassicSimilarity], result of:
          0.029515022 = score(doc=3089,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.19345059 = fieldWeight in 3089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3089)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

24. 8.2016 17:53:22