Search (11 results, page 1 of 1)

0.05337488 = product of:
  0.10674976 = sum of:
    0.10674976 = product of:
      0.21349952 = sum of:
        0.21349952 = weight(_text_:journals in 4257) [ClassicSimilarity], result of:
          0.21349952 = score(doc=4257,freq=18.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.83213156 = fieldWeight in 4257, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4257)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Although researchers have begun to investigate the difference in scientific impact between closed-access and open-access journals, studies that focus specifically on dynamic and disciplinary differences remain scarce. This study serves to fill this gap by using a large longitudinal dataset to examine these differences. Using CiteScore as a proxy for journal scientific impact, we employ a series of statistical tests to identify the quartile categories and disciplinary areas in which impact trends differ notably between closed- and open-access journals. We find that closed-access journals have a noticeable advantage in social sciences (for example, business and economics), whereas open-access journals perform well in medical and healthcare domains (for example, health profession and nursing). Moreover, we find that after controlling for a journal's rank and disciplinary differences, there are statistically more closed-access journals in the top 10%, Quartile 1, and Quartile 2 categories as measured by CiteScore; in contrast, more open-access journals in Quartile 4 gained scientific impact from 2011 to 2015. Considering dynamic and disciplinary trends in tandem, we find that more closed-access journals in Social Sciences gained in impact, whereas in biochemistry and medicine, more open-access journals experienced such gains.

0.036979202 = product of:
  0.073958404 = sum of:
    0.073958404 = product of:
      0.14791681 = sum of:
        0.14791681 = weight(_text_:journals in 4349) [ClassicSimilarity], result of:
          0.14791681 = score(doc=4349,freq=6.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.5765177 = fieldWeight in 4349, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.046875 = fieldNorm(doc=4349)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.

0.028466603 = product of:
  0.056933206 = sum of:
    0.056933206 = product of:
      0.11386641 = sum of:
        0.11386641 = weight(_text_:journals in 3705) [ClassicSimilarity], result of:
          0.11386641 = score(doc=3705,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.44380352 = fieldWeight in 3705, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.0625 = fieldNorm(doc=3705)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The authors propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weightedcitation captures prestige, whereas citation counts capture popularity. They compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.

0.024908276 = product of:
  0.049816553 = sum of:
    0.049816553 = product of:
      0.099633105 = sum of:
        0.099633105 = weight(_text_:journals in 3083) [ClassicSimilarity], result of:
          0.099633105 = score(doc=3083,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.38832808 = fieldWeight in 3083, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3083)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

0.021349952 = product of:
  0.042699903 = sum of:
    0.042699903 = product of:
      0.08539981 = sum of:
        0.08539981 = weight(_text_:journals in 2272) [ClassicSimilarity], result of:
          0.08539981 = score(doc=2272,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.33285263 = fieldWeight in 2272, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.046875 = fieldNorm(doc=2272)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal-based assessments. The aim of this article is to highlight topics as a valuable unit of analysis. A set of topic-based approaches is applied to a data set on library and information science publications. Results show that topic-based approaches are capable of revealing the research dynamics, impact, and dissemination of the selected data set. The article also identifies a nonsignificant relationship between topic popularity and impact and argues for the need to use both variables in describing topic characteristics. Additionally, a flow map illustrates critical topic-level knowledge dissemination channels.

0.021349952 = product of:
  0.042699903 = sum of:
    0.042699903 = product of:
      0.08539981 = sum of:
        0.08539981 = weight(_text_:journals in 3053) [ClassicSimilarity], result of:
          0.08539981 = score(doc=3053,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.33285263 = fieldWeight in 3053, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.046875 = fieldNorm(doc=3053)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The objective of this paper is to identify the dynamic structure of several time-dependent, discipline-level citation networks through a path-based method. A network data set is prepared that comprises 27 subjects and their citations aggregated from more than 27,000 journals and proceedings indexed in the Scopus database. A maximum spanning tree method is employed to extract paths in the weighted, directed, and cyclic networks. This paper finds that subjects such as Medicine, Biochemistry, Chemistry, Materials Science, Physics, and Social Sciences are the ones with multiple branches in the spanning tree. This paper also finds that most paths connect science, technology, engineering, and mathematics (STEM) fields; 2 critical paths connecting STEM and non-STEM fields are the one from Mathematics to Decision Sciences and the one from Medicine to Social Sciences.

0.021349952 = product of:
  0.042699903 = sum of:
    0.042699903 = product of:
      0.08539981 = sum of:
        0.08539981 = weight(_text_:journals in 3092) [ClassicSimilarity], result of:
          0.08539981 = score(doc=3092,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.33285263 = fieldWeight in 3092, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.046875 = fieldNorm(doc=3092)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This study examines patterns of dynamic disciplinary knowledge production and diffusion. It uses a citation data set of Scopus-indexed journals and proceedings. The journal-level citation data set is aggregated into 27 subject areas and these subjects are selected as the unit of analysis. A 3-step approach is employed: the first step examines disciplines' citation characteristics through scientific trading dimensions; the second step analyzes citation flows between pairs of disciplines; and the third step uses egocentric citation networks to assess individual disciplines' citation flow diversity through Shannon entropy. The results show that measured by scientific impact, the subjects of Chemical Engineering, Energy, and Environmental Science have the fastest growth. Furthermore, most subjects are carrying out more diversified knowledge trading practices by importing higher volumes of knowledge from a greater number of subjects. The study also finds that the growth rates of disciplinary citations align with the growth rates of global research and development (R&D) expenditures, thus providing evidence to support the impact of R&D expenditures on knowledge production.

0.017791625 = product of:
  0.03558325 = sum of:
    0.03558325 = product of:
      0.0711665 = sum of:
        0.0711665 = weight(_text_:journals in 4608) [ClassicSimilarity], result of:
          0.0711665 = score(doc=4608,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.2773772 = fieldWeight in 4608, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4608)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.

0.017791625 = product of:
  0.03558325 = sum of:
    0.03558325 = product of:
      0.0711665 = sum of:
        0.0711665 = weight(_text_:journals in 3633) [ClassicSimilarity], result of:
          0.0711665 = score(doc=3633,freq=2.0), product of:
            0.25656942 = queryWeight, product of:
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.05109862 = queryNorm
            0.2773772 = fieldWeight in 3633, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.021064 = idf(docFreq=792, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3633)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The objective of this paper is to systematically assess sources' (e.g., journals and proceedings) impact in knowledge trading. While there have been efforts at evaluating different aspects of journal impact, the dimension of knowledge trading is largely absent. To fill the gap, this study employed a set of trading-based indicators, including weighted degree centrality, Shannon entropy, and weighted betweenness centrality, to assess sources' trading impact. These indicators were applied to several time-sliced source-to-source citation networks that comprise 33,634 sources indexed in the Scopus database. The results show that several interdisciplinary sources, such as Nature, PLoS One, Proceedings of the National Academy of Sciences, and Science, and several specialty sources, such as Lancet, Lecture Notes in Computer Science, Journal of the American Chemical Society, Journal of Biological Chemistry, and New England Journal of Medicine, have demonstrated their marked importance in knowledge trading. Furthermore, this study also reveals that, overall, sources have established more trading partners, increased their trading volumes, broadened their trading areas, and diversified their trading contents over the past 15 years from 1997 to 2011. These results inform the understanding of source-level impact assessment and knowledge diffusion.

0.012238529 = product of:
  0.024477057 = sum of:
    0.024477057 = product of:
      0.048954114 = sum of:
        0.048954114 = weight(_text_:22 in 1534) [ClassicSimilarity], result of:
          0.048954114 = score(doc=1534,freq=4.0), product of:
            0.17893866 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05109862 = queryNorm
            0.27358043 = fieldWeight in 1534, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1534)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

26.10.2014 20:22:22

0.010384736 = product of:
  0.020769471 = sum of:
    0.020769471 = product of:
      0.041538943 = sum of:
        0.041538943 = weight(_text_:22 in 886) [ClassicSimilarity], result of:
          0.041538943 = score(doc=886,freq=2.0), product of:
            0.17893866 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05109862 = queryNorm
            0.23214069 = fieldWeight in 886, 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=886)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 1.2023 18:53:32