Search (52 results, page 2 of 3)

0.004371183 = product of:
  0.021855915 = sum of:
    0.021855915 = weight(_text_:of in 1857) [ClassicSimilarity], result of:
      0.021855915 = score(doc=1857,freq=30.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.33457235 = fieldWeight in 1857, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1857)
  0.2 = coord(1/5)

Abstract

We measure synergy for the Russian national, provincial, and regional innovation systems as reduction of uncertainty using mutual information among the 3 distributions of firm sizes, technological knowledge bases of firms, and geographical locations. Half a million units of data at firm level in 2011 were obtained from the OrbisT database of Bureau Van Dijk. The firm level data were aggregated at the levels of 8 Federal Districts, the regional level of 83 Federal Subjects, and the single level of the Russian Federation. Not surprisingly, the knowledge base of the economy is concentrated in the Moscow region (22.8%) and Saint Petersburg (4.0%). Except in Moscow itself, high-tech manufacturing does not add synergy to any other unit at any of the various levels of geographical granularity; instead it disturbs regional coordination. Knowledge-intensive services (KIS; including laboratories) contribute to the synergy in all Federal Districts (except the North-Caucasian Federal District), but only in 30 of the 83 Federal Subjects. The synergy in KIS is concentrated in centers of administration. The knowledge-intensive services (which are often state affiliated) provide backbone to an emerging knowledge-based economy at the level of Federal Districts, but the economy is otherwise not knowledge based (except for the Moscow region).

Source

Journal of the Association for Information Science and Technology. 66(2015) no.6, S.1229-1238

0.004282867 = product of:
  0.021414334 = sum of:
    0.021414334 = weight(_text_:of in 1047) [ClassicSimilarity], result of:
      0.021414334 = score(doc=1047,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 1047, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1047)
  0.2 = coord(1/5)

Abstract

Based on the complete set of firm data for Sweden (N = 1,187,421; November 2011), we analyze the mutual information among the geographical, technological, and organizational distributions in terms of synergies at regional and national levels. Using this measure, the interaction among three dimensions can become negative and thus indicate a net export of uncertainty by a system or, in other words, synergy in how knowledge functions are distributed over the carriers. Aggregation at the regional level (NUTS3) of the data organized at the municipal level (NUTS5) shows that 48.5% of the regional synergy is provided by the 3 metropolitan regions of Stockholm, Gothenburg, and Malmö/Lund. Sweden can be considered a centralized and hierarchically organized system. Our results accord with other statistics, but this triple helix indicator measures synergy more specifically and quantitatively. The analysis also provides us with validation for using this measure in previous studies of more regionalized systems of innovation (such as Hungary and Norway).

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1890-1902

0.004282867 = product of:
  0.021414334 = sum of:
    0.021414334 = weight(_text_:of in 2781) [ClassicSimilarity], result of:
      0.021414334 = score(doc=2781,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 2781, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2781)
  0.2 = coord(1/5)

Abstract

Using Web of Science data, portfolio analysis in terms of journal coverage can be projected onto a base map for units of analysis such as countries, cities, universities, and firms. The units of analysis under study can be compared statistically across the 10,000+ journals. The interdisciplinarity of the portfolios is measured using Rao-Stirling diversity or Zhang et?al.'s improved measure 2D3. At the country level we find regional differentiation (e.g., Latin American or Asian countries), but also a major divide between advanced and less-developed countries. Israel and Israeli cities outperform other nations and cities in terms of diversity. Universities appear to be specifically related to firms when a number of these units are exploratively compared. The instrument is relatively simple and straightforward, and one can generalize the application to any document set retrieved from the Web of Science (WoS). Further instruction is provided online at http://www.leydesdorff.net/portfolio.

Aid

Web of Science

Source

Journal of the Association for Information Science and Technology. 67(2016) no.3, S.741-748

0.004282867 = product of:
  0.021414334 = sum of:
    0.021414334 = weight(_text_:of in 3322) [ClassicSimilarity], result of:
      0.021414334 = score(doc=3322,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 3322, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3322)
  0.2 = coord(1/5)

Abstract

This paper examines the use of scientometric overlay mapping as a tool of "strategic intelligence" to aid the governing of emerging technologies. We develop an integrative synthesis of different overlay mapping techniques and associated perspectives on technological emergence across geographical, social, and cognitive spaces. To do so, we longitudinally analyze (with publication and patent data) three case studies of emerging technologies in the medical domain. These are RNA interference (RNAi), human papillomavirus (HPV) testing technologies for cervical cancer, and thiopurine methyltransferase (TPMT) genetic testing. Given the flexibility (i.e., adaptability to different sources of data) and granularity (i.e., applicability across multiple levels of data aggregation) of overlay mapping techniques, we argue that these techniques can favor the integration and comparison of results from different contexts and cases, thus potentially functioning as a platform for "distributed" strategic intelligence for analysts and decision makers.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.214-233

0.0042229644 = product of:
  0.02111482 = sum of:
    0.02111482 = weight(_text_:of in 4941) [ClassicSimilarity], result of:
      0.02111482 = score(doc=4941,freq=28.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32322758 = fieldWeight in 4941, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4941)
  0.2 = coord(1/5)

Abstract

Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping techniques previously developed for mapping journal structures in the Science and Social Sciences Citation Indices. Citation relations among the 110,718 records were aggregated at the level of 1,157 journals specific to the A&HCI, and the journal structures are questioned on whether a cognitive structure can be reconstructed and visualized. Both cosine-normalization (bottom up) and factor analysis (top down) suggest a division into approximately 12 subsets. The relations among these subsets are explored using various visualization techniques. However, we were not able to retrieve this structure using the Institute for Scientific Information Subject Categories, including the 25 categories that are specific to the A&HCI. We discuss options for validation such as against the categories of the Humanities Indicators of the American Academy of Arts and Sciences, the panel structure of the European Reference Index for the Humanities, and compare our results with the curriculum organization of the Humanities Section of the College of Letters and Sciences of the University of California at Los Angeles as an example of institutional organization.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.12, S.2414-2426

0.0042229644 = product of:
  0.02111482 = sum of:
    0.02111482 = weight(_text_:of in 1211) [ClassicSimilarity], result of:
      0.02111482 = score(doc=1211,freq=28.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32322758 = fieldWeight in 1211, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1211)
  0.2 = coord(1/5)

Abstract

The study of interhuman communication requires a more complex framework than Claude E. Shannon's (1948) mathematical theory of communication because "information" is defined in the latter case as meaningless uncertainty. Assuming that meaning cannot be communicated, we extend Shannon's theory by defining mutual redundancy as a positional counterpart of the relational communication of information. Mutual redundancy indicates the surplus of meanings that can be provided to the exchanges in reflexive communications. The information is redundant because it is based on "pure sets" (i.e., without subtraction of mutual information in the overlaps). We show that in the three-dimensional case (e.g., of a triple helix of university-industry-government relations), mutual redundancy is equal to mutual information (Rxyz = Txyz); but when the dimensionality is even, the sign is different. We generalize to the measurement in N dimensions and proceed to the interpretation. Using Niklas Luhmann's (1984-1995) social systems theory and/or Anthony Giddens's (1979, 1984) structuration theory, mutual redundancy can be provided with an interpretation in the sociological case: Different meaning-processing structures code and decode with other algorithms. A surplus of ("absent") options can then be generated that add to the redundancy. Luhmann's "functional (sub)systems" of expectations or Giddens's "rule-resource sets" are positioned mutually, but coupled operationally in events or "instantiated" in actions. Shannon-type information is generated by the mediation, but the "structures" are (re-)positioned toward one another as sets of (potentially counterfactual) expectations. The structural differences among the coding and decoding algorithms provide a source of additional options in reflexive and anticipatory communications.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.386-399

0.0042229644 = product of:
  0.02111482 = sum of:
    0.02111482 = weight(_text_:of in 1241) [ClassicSimilarity], result of:
      0.02111482 = score(doc=1241,freq=28.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32322758 = fieldWeight in 1241, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1241)
  0.2 = coord(1/5)

Abstract

Group-based trajectory modeling (GBTM) is applied to the citation curves of articles in six journals and to all citable items in a single field of science (virology, 24 journals) to distinguish among the developmental trajectories in subpopulations. Can citation patterns of highly-cited papers be distinguished in an early phase as "fast-breaking" papers? Can "late bloomers" or "sleeping beauties" be identified? Most interesting, we find differences between "sticky knowledge claims" that continue to be cited more than 10 years after publication and "transient knowledge claims" that show a decay pattern after reaching a peak within a few years. Only papers following the trajectory of a "sticky knowledge claim" can be expected to have a sustained impact. These findings raise questions about indicators of "excellence" that use aggregated citation rates after 2 or 3 years (e.g., impact factors). Because aggregated citation curves can also be composites of the two patterns, fifth-order polynomials (with four bending points) are needed to capture citation curves precisely. For the journals under study, the most frequently cited groups were furthermore much smaller than 10%. Although GBTM has proved a useful method for investigating differences among citation trajectories, the methodology does not allow us to define a percentage of highly cited papers inductively across different fields and journals. Using multinomial logistic regression, we conclude that predictor variables such as journal names, number of authors, etc., do not affect the stickiness of knowledge claims in terms of citations but only the levels of aggregated citations (which are field-specific).

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.797-811

0.00406935 = product of:
  0.02034675 = sum of:
    0.02034675 = weight(_text_:of in 532) [ClassicSimilarity], result of:
      0.02034675 = score(doc=532,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 532, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=532)
  0.2 = coord(1/5)

Abstract

Using the CD-ROM version of the Science Citation Index 2010 (N = 3,705 journals), we study the (combined) effects of (a) fractional counting on the impact factor (IF) and (b) transformation of the skewed citation distributions into a distribution of 100 percentiles and six percentile rank classes (top-1%, top-5%, etc.). Do these approaches lead to field-normalized impact measures for journals? In addition to the 2-year IF (IF2), we consider the 5-year IF (IF5), the respective numerators of these IFs, and the number of Total Cites, counted both as integers and fractionally. These various indicators are tested against the hypothesis that the classification of journals into 11 broad fields by PatentBoard/NSF (National Science Foundation) provides statistically significant between-field effects. Using fractional counting the between-field variance is reduced by 91.7% in the case of IF5, and by 79.2% in the case of IF2. However, the differences in citation counts are not significantly affected by fractional counting. These results accord with previous studies, but the longer citation window of a fractionally counted IF5 can lead to significant improvement in the normalization across fields.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.1, S.96-107

0.00406935 = product of:
  0.02034675 = sum of:
    0.02034675 = weight(_text_:of in 2047) [ClassicSimilarity], result of:
      0.02034675 = score(doc=2047,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 2047, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2047)
  0.2 = coord(1/5)

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.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.7, S.1507-1513

0.00406935 = product of:
  0.02034675 = sum of:
    0.02034675 = weight(_text_:of in 3090) [ClassicSimilarity], result of:
      0.02034675 = score(doc=3090,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 3090, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3090)
  0.2 = coord(1/5)

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.

Object

Web of science

Source

Journal of the Association for Information Science and Technology. 67(2016) no.9, S.2194-2211

0.00406935 = product of:
  0.02034675 = sum of:
    0.02034675 = weight(_text_:of in 3328) [ClassicSimilarity], result of:
      0.02034675 = score(doc=3328,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 3328, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3328)
  0.2 = coord(1/5)

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.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.197-213

0.00406935 = product of:
  0.02034675 = sum of:
    0.02034675 = weight(_text_:of in 5390) [ClassicSimilarity], result of:
      0.02034675 = score(doc=5390,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 5390, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5390)
  0.2 = coord(1/5)

Abstract

Using information theory, we measure innovation systemness as synergy among size-classes, ZIP Codes, and technological classes (NACE-codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core-Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high-tech manufacturing (HTM), medium-high-tech manufacturing (MHTM), knowledge-intensive services (KIS), and high-tech services (HTKIS) does not change this pattern, but refines it. The East Coast-New Jersey, Boston, and New York-and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. KIS in Silicon Valley and the Bay Area-a CSA composed of seven CBSA-spill over to other regions and even globally.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1108-1123

0.0040630843 = product of:
  0.02031542 = sum of:
    0.02031542 = weight(_text_:of in 494) [ClassicSimilarity], result of:
      0.02031542 = score(doc=494,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3109903 = fieldWeight in 494, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=494)
  0.2 = coord(1/5)

Abstract

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the MEDLINE database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the MEDLINE database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the MEDLINE database on one hand, and the Science Citation Index and Scopus (containing citation information) on the other.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.11, S.2239-2253

0.0040630843 = product of:
  0.02031542 = sum of:
    0.02031542 = weight(_text_:of in 1110) [ClassicSimilarity], result of:
      0.02031542 = score(doc=1110,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3109903 = fieldWeight in 1110, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1110)
  0.2 = coord(1/5)

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.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2317-2325

0.0040630843 = product of:
  0.02031542 = sum of:
    0.02031542 = weight(_text_:of in 2779) [ClassicSimilarity], result of:
      0.02031542 = score(doc=2779,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3109903 = fieldWeight in 2779, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2779)
  0.2 = coord(1/5)

Abstract

Normalization of citation scores using reference sets based on Web of Science subject categories (WCs) has become an established ("best") practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC "information science & library science" and the WCs attributed to journals in the field of "science and technology studies," we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of "indexer effects." Can the compliance with "best practices" be replaced with an ambition to develop "best possible practices"? New research questions can then be envisaged.

Aid

Web of Science

Source

Journal of the Association for Information Science and Technology. 67(2016) no.3, S.707-714

0.003909705 = product of:
  0.019548526 = sum of:
    0.019548526 = weight(_text_:of in 1131) [ClassicSimilarity], result of:
      0.019548526 = score(doc=1131,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2992506 = fieldWeight in 1131, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1131)
  0.2 = coord(1/5)

Abstract

Using the option Analyze Results with the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Sciences Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy" (Izsák & Papp, 1995). Since this indicator of interdisciplinarity is normalized between 0 and 1, interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel, Guillaume, Lambiotte, and Lefebvre's (2008) community-finding algorithms operating on the relations among journals included in the JCR. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: Local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.12, S.2573-2586

0.003909705 = product of:
  0.019548526 = sum of:
    0.019548526 = weight(_text_:of in 1237) [ClassicSimilarity], result of:
      0.019548526 = score(doc=1237,freq=24.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2992506 = fieldWeight in 1237, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1237)
  0.2 = coord(1/5)

Abstract

The h-index provides us with 9 natural classes which can be written as a matrix of 3 vectors. The 3 vectors are: X = (X1, X2, X3) and indicates publication distribution in the h-core, the h-tail, and the uncited ones, respectively; Y = (Y1, Y2, Y3) denotes the citation distribution of the h-core, the h-tail and the so-called "excess" citations (above the h-threshold), respectively; and Z = (Z1, Z2, Z3) = (Y1-X1, Y2-X2, Y3-X3). The matrix V = (X,Y,Z)T constructs a measure of academic performance, in which the 9 numbers can all be provided with meanings in different dimensions. The "academic trace" tr(V) of this matrix follows naturally, and contributes a unique indicator for total academic achievements by summarizing and weighting the accumulation of publications and citations. This measure can also be used to combine the advantages of the h-index and the integrated impact indicator (I3) into a single number with a meaningful interpretation of the values. We illustrate the use of tr(V) for the cases of 2 journal sets, 2 universities, and ourselves as 2 individual authors.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.742-750

0.0038307128 = product of:
  0.019153563 = sum of:
    0.019153563 = weight(_text_:of in 447) [ClassicSimilarity], result of:
      0.019153563 = score(doc=447,freq=4.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.2932045 = fieldWeight in 447, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=447)
  0.2 = coord(1/5)

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.11, S.2349-2350

0.0037432574 = product of:
  0.018716287 = sum of:
    0.018716287 = weight(_text_:of in 1505) [ClassicSimilarity], result of:
      0.018716287 = score(doc=1505,freq=22.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.28651062 = fieldWeight in 1505, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1505)
  0.2 = coord(1/5)

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.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.10, S.2111-2126

0.0037432574 = product of:
  0.018716287 = sum of:
    0.018716287 = weight(_text_:of in 1814) [ClassicSimilarity], result of:
      0.018716287 = score(doc=1814,freq=22.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.28651062 = fieldWeight in 1814, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1814)
  0.2 = coord(1/5)

Abstract

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals?=?20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web of Science (N?=?10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.5, S.1001-1016