Search (225 results, page 1 of 12)

0.06357456 = product of:
  0.095361836 = sum of:
    0.067894526 = weight(_text_:bibliographic in 3691) [ClassicSimilarity], result of:
      0.067894526 = score(doc=3691,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.34409973 = fieldWeight in 3691, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0625 = fieldNorm(doc=3691)
    0.02746731 = product of:
      0.05493462 = sum of:
        0.05493462 = weight(_text_:22 in 3691) [ClassicSimilarity], result of:
          0.05493462 = score(doc=3691,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.30952093 = fieldWeight in 3691, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=3691)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

International bibliographic databases and related biblimetric indicators together provide an analytical framework and appropriate measure to cover both the 'supply side' - research capabilities and outputs - and 'demand side' - collaboration, diffusion and citation impact - related to information and communication technologies (ICT) research. Presents results of such a bibliometric study describing macro level features of this ICT knowledge base

Date

22. 5.1999 19:26:54

0.056010127 = product of:
  0.16803038 = sum of:
    0.16803038 = weight(_text_:bibliographic in 3792) [ClassicSimilarity], result of:
      0.16803038 = score(doc=3792,freq=4.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.8516034 = fieldWeight in 3792, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.109375 = fieldNorm(doc=3792)
  0.33333334 = coord(1/3)

Source

International cataloguing and bibliographic control. 28(1999) no.3, S.76-77

0.051451948 = product of:
  0.07717792 = sum of:
    0.06001085 = weight(_text_:bibliographic in 4635) [ClassicSimilarity], result of:
      0.06001085 = score(doc=4635,freq=4.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.30414405 = fieldWeight in 4635, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4635)
    0.017167069 = product of:
      0.034334138 = sum of:
        0.034334138 = weight(_text_:22 in 4635) [ClassicSimilarity], result of:
          0.034334138 = score(doc=4635,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.19345059 = fieldWeight in 4635, 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=4635)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

We investigate the extent to which open-access (OA) journals and articles in biology, computer science, economics, history, medicine, and psychology are indexed in each of 11 bibliographic databases. We also look for variations in index coverage by journal subject, journal size, publisher type, publisher size, date of first OA issue, region of publication, language of publication, publication fee, and citation impact factor. Two databases, Biological Abstracts and PubMed, provide very good coverage of the OA journal literature, indexing 60 to 63% of all OA articles in their disciplines. Five databases provide moderately good coverage (22-41%), and four provide relatively poor coverage (0-12%). OA articles in biology journals, English-only journals, high-impact journals, and journals that charge publication fees of $1,000 or more are especially likely to be indexed. Conversely, articles from OA publishers in Africa, Asia, or Central/South America are especially unlikely to be indexed. Four of the 11 databases index commercially published articles at a substantially higher rate than articles published by universities, scholarly societies, nonprofit publishers, or governments. Finally, three databases-EBSCO Academic Search Complete, ProQuest Research Library, and Wilson OmniFile-provide less comprehensive coverage of OA articles than of articles in comparable subscription journals.

0.051451948 = product of:
  0.07717792 = sum of:
    0.06001085 = weight(_text_:bibliographic in 4959) [ClassicSimilarity], result of:
      0.06001085 = score(doc=4959,freq=4.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.30414405 = fieldWeight in 4959, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4959)
    0.017167069 = product of:
      0.034334138 = sum of:
        0.034334138 = weight(_text_:22 in 4959) [ClassicSimilarity], result of:
          0.034334138 = score(doc=4959,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.19345059 = fieldWeight in 4959, 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=4959)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This study uses three bibliometric methods: direct citation, bibliographic coupling, and co-authorship analysis, to investigate interdisciplinary changes in library and information science (LIS) from 1978 to 2007. The results reveal that LIS researchers most frequently cite publications in their own discipline. In addition, half of all co-authors of LIS articles are affiliated with LIS-related institutes. The results confirm that the degree of interdisciplinarity within LIS has increased, particularly co-authorship. However, the study found sources of direct citations in LIS articles are widely distributed across 30 disciplines, but co-authors of LIS articles are distributed across only 25 disciplines. The degree of interdisciplinarity was found ranging from 0.61 to 0.82 with citation to references in all articles being the highest and that of co-authorship being the lowest. Percentages of contribution attributable to LIS show a decreasing tendency based on the results of direct citation and co-authorship analysis, but an increasing tendency based on those of bibliographic coupling analysis. Such differences indicate each of the three bibliometric methods has its strength and provides insights respectively for viewing various aspects of interdisciplinarity, suggesting the use of no single bibliometric method can reveal all aspects of interdisciplinarity due to its multifaceted nature.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33

0.049034476 = product of:
  0.073551714 = sum of:
    0.025460448 = weight(_text_:bibliographic in 3809) [ClassicSimilarity], result of:
      0.025460448 = score(doc=3809,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.1290374 = fieldWeight in 3809, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3809)
    0.048091263 = sum of:
      0.027490778 = weight(_text_:searching in 3809) [ClassicSimilarity], result of:
        0.027490778 = score(doc=3809,freq=2.0), product of:
          0.20502694 = queryWeight, product of:
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.05068286 = queryNorm
          0.13408373 = fieldWeight in 3809, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.0234375 = fieldNorm(doc=3809)
      0.020600483 = weight(_text_:22 in 3809) [ClassicSimilarity], result of:
        0.020600483 = score(doc=3809,freq=2.0), product of:
          0.17748274 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05068286 = queryNorm
          0.116070345 = fieldWeight in 3809, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=3809)
  0.6666667 = coord(2/3)

Abstract

One of the solutions to help scientists filter the most relevant publications and, thus, to stay current on developments in their fields during the transition from "little science" to "big science", was the introduction of citation indexing as a Wellsian "World Brain" (Garfield, 1964) of scientific information: It is too much to expect a research worker to spend an inordinate amount of time searching for the bibliographic descendants of antecedent papers. It would not be excessive to demand that the thorough scholar check all papers that have cited or criticized such papers, if they could be located quickly. The citation index makes this check practicable (Garfield, 1955, p. 108). In retrospective, citation indexing can be perceived as a pre-social web version of crowdsourcing, as it is based on the concept that the community of citing authors outperforms indexers in highlighting cognitive links between papers, particularly on the level of specific ideas and concepts (Garfield, 1983). Over the last 50 years, citation analysis and more generally, bibliometric methods, have developed from information retrieval tools to research evaluation metrics, where they are presumed to make scientific funding more efficient and effective (Moed, 2006). However, the dominance of bibliometric indicators in research evaluation has also led to significant goal displacement (Merton, 1957) and the oversimplification of notions of "research productivity" and "scientific quality", creating adverse effects such as salami publishing, honorary authorships, citation cartels, and misuse of indicators (Binswanger, 2015; Cronin and Sugimoto, 2014; Frey and Osterloh, 2006; Haustein and Larivière, 2015; Weingart, 2005).

Date

20. 1.2015 18:30:22

0.04547727 = product of:
  0.13643181 = sum of:
    0.13643181 = sum of:
      0.09523085 = weight(_text_:searching in 2742) [ClassicSimilarity], result of:
        0.09523085 = score(doc=2742,freq=6.0), product of:
          0.20502694 = queryWeight, product of:
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.05068286 = queryNorm
          0.46447968 = fieldWeight in 2742, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.046875 = fieldNorm(doc=2742)
      0.041200966 = weight(_text_:22 in 2742) [ClassicSimilarity], result of:
        0.041200966 = score(doc=2742,freq=2.0), product of:
          0.17748274 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05068286 = queryNorm
          0.23214069 = fieldWeight in 2742, 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=2742)
  0.33333334 = coord(1/3)

Abstract

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.

Date

22. 3.2009 17:49:11

0.041576736 = product of:
  0.12473021 = sum of:
    0.12473021 = weight(_text_:bibliographic in 2840) [ClassicSimilarity], result of:
      0.12473021 = score(doc=2840,freq=12.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.63215154 = fieldWeight in 2840, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.046875 = fieldNorm(doc=2840)
  0.33333334 = coord(1/3)

Abstract

This paper explores the ISI Journal Citation Reports (JCR) bibliographic and subject structures through Library of Congress (LC) and American research libraries cataloging and classification methodology. The 2006 Science Citation Index JCR Behavioral Sciences subject category journals are used as an example. From the library perspective, the main fault of the JCR bibliographic structure is that the JCR mistakenly identifies journal title segments as journal bibliographic entities, seriously affecting journal rankings by total cites and the impact factor. In respect to JCR subject structure, the title segment, which constitutes the JCR bibliographic basis, is posited as the best bibliographic entity for the citation measurement of journal subject relationships. Through factor analysis and other methods, the JCR subject categorization of journals is tested against their LC subject headings and classification. The finding is that JCR and library journal subject analyses corroborate, clarify, and correct each other.

0.040007234 = product of:
  0.1200217 = sum of:
    0.1200217 = weight(_text_:bibliographic in 917) [ClassicSimilarity], result of:
      0.1200217 = score(doc=917,freq=16.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.6082881 = fieldWeight in 917, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=917)
  0.33333334 = coord(1/3)

Abstract

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph-based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real-world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation-based methods were applied to the entities and relationships to generate their low-dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation-based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.

0.039734103 = product of:
  0.05960115 = sum of:
    0.04243408 = weight(_text_:bibliographic in 2743) [ClassicSimilarity], result of:
      0.04243408 = score(doc=2743,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.21506234 = fieldWeight in 2743, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2743)
    0.017167069 = product of:
      0.034334138 = sum of:
        0.034334138 = weight(_text_:22 in 2743) [ClassicSimilarity], result of:
          0.034334138 = score(doc=2743,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.19345059 = fieldWeight in 2743, 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=2743)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component.

Date

22. 3.2009 17:52:50

0.039734103 = product of:
  0.05960115 = sum of:
    0.04243408 = weight(_text_:bibliographic in 4150) [ClassicSimilarity], result of:
      0.04243408 = score(doc=4150,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.21506234 = fieldWeight in 4150, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4150)
    0.017167069 = product of:
      0.034334138 = sum of:
        0.034334138 = weight(_text_:22 in 4150) [ClassicSimilarity], result of:
          0.034334138 = score(doc=4150,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.19345059 = fieldWeight in 4150, 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=4150)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The domain of knowledge organization (KO) represents a foundational area of information science. One way to better understand the intellectual structure of the KO domain is to apply bibliometric methods to key contributors to the literature. This study analyzes the most prolific contributing authors to the journal Knowledge Organization, the sources they cite and the citations they receive for the period 1993 to 2016. The analyses were conducted using visualization outcomes of citation, co-citation and author bibliographic coupling analysis to reveal theoretical points of reference among authors and the most prominent research themes that constitute this scientific community. Birger Hjørland was the most cited author, and was situated at or near the middle of each of the maps based on different citation relationships. The proximities between authors resulting from the different citation relationships demonstrate how authors situate themselves intellectually through the citations they give and how other authors situate them through the citations received. There is a consistent core of theoretical references as well among the most productive authors. We observed a close network of scholarly communication between the authors cited in this core, which indicates the actual role of the journal Knowledge Organization as a space for knowledge construction in the area of knowledge organization.

Source

Knowledge organization. 45(2018) no.1, S.13-22

0.03178728 = product of:
  0.047680918 = sum of:
    0.033947263 = weight(_text_:bibliographic in 5171) [ClassicSimilarity], result of:
      0.033947263 = score(doc=5171,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.17204987 = fieldWeight in 5171, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03125 = fieldNorm(doc=5171)
    0.013733655 = product of:
      0.02746731 = sum of:
        0.02746731 = weight(_text_:22 in 5171) [ClassicSimilarity], result of:
          0.02746731 = score(doc=5171,freq=2.0), product of:
            0.17748274 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05068286 = queryNorm
            0.15476047 = fieldWeight in 5171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=5171)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Ahlgren, Jarneving, and. Rousseau review accepted procedures for author co-citation analysis first pointing out that since in the raw data matrix the row and column values are identical i,e, the co-citation count of two authors, there is no clear choice for diagonal values. They suggest the number of times an author has been co-cited with himself excluding self citation rather than the common treatment as zeros or as missing values. When the matrix is converted to a similarity matrix the normal procedure is to create a matrix of Pearson's r coefficients between data vectors. Ranking by r and by co-citation frequency and by intuition can easily yield three different orders. It would seem necessary that the adding of zeros to the matrix will not affect the value or the relative order of similarity measures but it is shown that this is not the case with Pearson's r. Using 913 bibliographic descriptions form the Web of Science of articles form JASIS and Scientometrics, authors names were extracted, edited and 12 information retrieval authors and 12 bibliometric authors each from the top 100 most cited were selected. Co-citation and r value (diagonal elements treated as missing) matrices were constructed, and then reconstructed in expanded form. Adding zeros can both change the r value and the ordering of the authors based upon that value. A chi-squared distance measure would not violate these requirements, nor would the cosine coefficient. It is also argued that co-citation data is ordinal data since there is no assurance of an absolute zero number of co-citations, and thus Pearson is not appropriate. The number of ties in co-citation data make the use of the Spearman rank order coefficient problematic.

Date

9. 7.2006 10:22:35

0.031628497 = product of:
  0.09488549 = sum of:
    0.09488549 = weight(_text_:bibliographic in 4111) [ClassicSimilarity], result of:
      0.09488549 = score(doc=4111,freq=10.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.480894 = fieldWeight in 4111, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4111)
  0.33333334 = coord(1/3)

Abstract

In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004-2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92% of the corpus. Accuracies are compared using two metrics-within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used.

0.029938674 = product of:
  0.08981602 = sum of:
    0.08981602 = weight(_text_:bibliographic in 608) [ClassicSimilarity], result of:
      0.08981602 = score(doc=608,freq=14.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.45520115 = fieldWeight in 608, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03125 = fieldNorm(doc=608)
  0.33333334 = coord(1/3)

Abstract

Purpose Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data. Design/methodology/approach We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics. Findings Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not. Originality/value Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.

0.029399194 = product of:
  0.08819758 = sum of:
    0.08819758 = weight(_text_:bibliographic in 274) [ClassicSimilarity], result of:
      0.08819758 = score(doc=274,freq=6.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.44699866 = fieldWeight in 274, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.046875 = fieldNorm(doc=274)
  0.33333334 = coord(1/3)

Abstract

This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.

0.029399194 = product of:
  0.08819758 = sum of:
    0.08819758 = weight(_text_:bibliographic in 686) [ClassicSimilarity], result of:
      0.08819758 = score(doc=686,freq=6.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.44699866 = fieldWeight in 686, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.046875 = fieldNorm(doc=686)
  0.33333334 = coord(1/3)

Abstract

Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.

0.029399194 = product of:
  0.08819758 = sum of:
    0.08819758 = weight(_text_:bibliographic in 1036) [ClassicSimilarity], result of:
      0.08819758 = score(doc=1036,freq=6.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.44699866 = fieldWeight in 1036, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.046875 = fieldNorm(doc=1036)
  0.33333334 = coord(1/3)

Abstract

Historically, co-citation models have been based only on bibliographic information. Full-text analysis offers the opportunity to significantly improve the quality of the signals upon which these co-citation models are based. In this work we study the effect of reference proximity on the accuracy of co-citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co-citation clustering using only the bibliographic information to results from co-citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co-citation cluster solution by up to 30% over the traditional approach based on bibliographic information.

0.028289389 = product of:
  0.08486816 = sum of:
    0.08486816 = weight(_text_:bibliographic in 4542) [ClassicSimilarity], result of:
      0.08486816 = score(doc=4542,freq=2.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.43012467 = fieldWeight in 4542, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.078125 = fieldNorm(doc=4542)
  0.33333334 = coord(1/3)

Abstract

The reciprocal relationship between bibliographic references and citations in the context of the scholarly communication system is examined. Semiotic analysis of referencing behaviours and citation counting reveals the complexity of prevailing sign systems and associated symbolic practices.

0.028289389 = product of:
  0.08486816 = sum of:
    0.08486816 = weight(_text_:bibliographic in 1201) [ClassicSimilarity], result of:
      0.08486816 = score(doc=1201,freq=8.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.43012467 = fieldWeight in 1201, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1201)
  0.33333334 = coord(1/3)

Abstract

This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions-non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%-46%), and internal requirements that exclude secondary content (6.5%-11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.

0.028005064 = product of:
  0.08401519 = sum of:
    0.08401519 = weight(_text_:bibliographic in 5064) [ClassicSimilarity], result of:
      0.08401519 = score(doc=5064,freq=4.0), product of:
        0.19731061 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.05068286 = queryNorm
        0.4258017 = fieldWeight in 5064, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5064)
  0.33333334 = coord(1/3)

Abstract

The quality of online and CD-ROM databases is far from satisfactory. Errors are frequently found in listings from online searches. Spelling mistakes are the most common but there are also more misleading errors such as variations of an author's name or absence of homogenity in the content of certain field. Describes breifly a bibliometric study of large amounts of data downloaded from databases to investigate bibliographic errors and data heterogeneity. Recommends that database producers should consider either the implementation of a common format or the recommendations of the Société Française de Bibliométrie

0.026717365 = product of:
  0.080152094 = sum of:
    0.080152094 = sum of:
      0.04581796 = weight(_text_:searching in 882) [ClassicSimilarity], result of:
        0.04581796 = score(doc=882,freq=2.0), product of:
          0.20502694 = queryWeight, product of:
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.05068286 = queryNorm
          0.22347288 = fieldWeight in 882, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.0452914 = idf(docFreq=2103, maxDocs=44218)
            0.0390625 = fieldNorm(doc=882)
      0.034334138 = weight(_text_:22 in 882) [ClassicSimilarity], result of:
        0.034334138 = score(doc=882,freq=2.0), product of:
          0.17748274 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05068286 = queryNorm
          0.19345059 = fieldWeight in 882, 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=882)
  0.33333334 = coord(1/3)

Abstract

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Date

22. 1.2023 18:37:33