Search (56 results, page 1 of 3)

0.0047366275 = product of:
  0.03315639 = sum of:
    0.008353474 = weight(_text_:information in 1022) [ClassicSimilarity], result of:
      0.008353474 = score(doc=1022,freq=8.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.19395474 = fieldWeight in 1022, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1022)
    0.024802918 = weight(_text_:retrieval in 1022) [ClassicSimilarity], result of:
      0.024802918 = score(doc=1022,freq=8.0), product of:
        0.07421378 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02453417 = queryNorm
        0.33420905 = fieldWeight in 1022, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1022)
  0.14285715 = coord(2/14)

Abstract

Bibliometric-enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data-driven approach, this article describes the development of a bibliometric-enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric-enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known-item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1010-1025

0.0041704755 = product of:
  0.029193329 = sum of:
    0.02328653 = weight(_text_:system in 176) [ClassicSimilarity], result of:
      0.02328653 = score(doc=176,freq=6.0), product of:
        0.07727166 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.02453417 = queryNorm
        0.30135927 = fieldWeight in 176, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=176)
    0.005906798 = weight(_text_:information in 176) [ClassicSimilarity], result of:
      0.005906798 = score(doc=176,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.13714671 = fieldWeight in 176, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=176)
  0.14285715 = coord(2/14)

Abstract

Purpose This paper aims to understand the nature of citations and metrics in the larger system of knowledge production involving universities, funding agencies, publishers, and indexing and data analytic services. Design/methodology/approach First, the normative and social constructivist views of citations are reviewed to be understood as co-existing conditions. Second, metrics are examined through the processes of commensuration by tracing the meanings of metrics embedded in various kinds of documents and contexts. Third, the steering effects of citations and metrics on knowledge production are discussed. Finally, the conclusion addresses questions pertaining to the validity and legitimacy of citations as data and their implications for knowledge production and the conception of information. Findings The normative view of citations is understood as an ideal speech situation; the social constructivist view of citation is recognised in the system of knowledge production where citing motivations are influenced by epistemic, social and political factors. When organisational performances are prioritised and generate system imperatives, motives of competition become dominant in shaping citing behaviour, which can deviate from the norms and values in the academic lifeworld. As a result, citations and metrics become a non-linguistic steering medium rather than evidence of research quality and impact. Originality/value This paper contributes to the understanding of the nature of citations and metrics and their implications for the conception of information and knowledge production.

0.0029541154 = product of:
  0.020678807 = sum of:
    0.013444485 = weight(_text_:system in 208) [ClassicSimilarity], result of:
      0.013444485 = score(doc=208,freq=2.0), product of:
        0.07727166 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.02453417 = queryNorm
        0.17398985 = fieldWeight in 208, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=208)
    0.0072343214 = weight(_text_:information in 208) [ClassicSimilarity], result of:
      0.0072343214 = score(doc=208,freq=6.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.16796975 = fieldWeight in 208, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=208)
  0.14285715 = coord(2/14)

Abstract

Problem solving often requires crossing boundaries, such as those between disciplines. When policy-makers call for "interdisciplinarity," however, they often mean "synergy." Synergy is generated when the whole offers more possibilities than the sum of its parts. An increase in the number of options above the sum of the options in subsets can be measured as redundancy; that is, the number of not-yet-realized options. The number of options available to an innovation system for realization can be as decisive for the system's survival as the historically already-realized innovations. Unlike "interdisciplinarity," "synergy" can also be generated in sectorial or geographical collaborations. The measurement of "synergy," however, requires a methodology different from the measurement of "interdisciplinarity." In this study, we discuss recent advances in the operationalization and measurement of "interdisciplinarity," and propose a methodology for measuring "synergy" based on information theory. The sharing of meanings attributed to information from different perspectives can increase redundancy. Increasing redundancy reduces the relative uncertainty, for example, in niches. The operationalization of the two concepts-"interdisciplinarity" and "synergy"-as different and partly overlapping indicators allows for distinguishing between the effects and the effectiveness of science-policy interventions in research priorities.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.4, S.387-402

0.0028493337 = product of:
  0.019945335 = sum of:
    0.0100241685 = weight(_text_:information in 610) [ClassicSimilarity], result of:
      0.0100241685 = score(doc=610,freq=18.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.23274568 = fieldWeight in 610, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=610)
    0.009921167 = weight(_text_:retrieval in 610) [ClassicSimilarity], result of:
      0.009921167 = score(doc=610,freq=2.0), product of:
        0.07421378 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02453417 = queryNorm
        0.13368362 = fieldWeight in 610, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=610)
  0.14285715 = coord(2/14)

Abstract

Purpose This study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011-2020. It also examines changes in this structure from 2006-2010 through 2011-2015 to 2016-2020. Results will contribute to a better understanding of the information science research field. Design/methodology/approach The well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011-2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor. Findings The two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011-2015 but shared dominance with information behavior during 2016-2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016-2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011-2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016-2020. Originality/value Author co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

0.0028433804 = product of:
  0.019903662 = sum of:
    0.008269517 = weight(_text_:information in 547) [ClassicSimilarity], result of:
      0.008269517 = score(doc=547,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.1920054 = fieldWeight in 547, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=547)
    0.011634145 = product of:
      0.02326829 = sum of:
        0.02326829 = weight(_text_:22 in 547) [ClassicSimilarity], result of:
          0.02326829 = score(doc=547,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.2708308 = fieldWeight in 547, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=547)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Abstract

This article discusses how letters to the editor boost publishing metrics for journals and authors, and then examines letters published since 2015 in six elite journals, including the Journal of the Association for Information Science and Technology. The initial findings identify some potentially anomalous use of letters and unusual self-citation patterns. The article proposes that Clarivate Analytics consider slightly reconfiguring the Journal Impact Factor to more fairly account for letters and that journals transparently explain their letter submission policies.

Date

6. 4.2022 19:22:26

Source

Journal of the Association for Information Science and Technology. 73(2022) no.5, S.702-707

0.0028419765 = product of:
  0.019893834 = sum of:
    0.0050120843 = weight(_text_:information in 605) [ClassicSimilarity], result of:
      0.0050120843 = score(doc=605,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.116372846 = fieldWeight in 605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=605)
    0.014881751 = weight(_text_:retrieval in 605) [ClassicSimilarity], result of:
      0.014881751 = score(doc=605,freq=2.0), product of:
        0.07421378 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02453417 = queryNorm
        0.20052543 = fieldWeight in 605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=605)
  0.14285715 = coord(2/14)

Abstract

The data re-collection for tweets from data snapshots is a common methodological step in Twitter-based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID-19-related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. "Revived" tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1192-1195

0.0028051115 = product of:
  0.01963578 = sum of:
    0.0072343214 = weight(_text_:information in 949) [ClassicSimilarity], result of:
      0.0072343214 = score(doc=949,freq=6.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.16796975 = fieldWeight in 949, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=949)
    0.012401459 = weight(_text_:retrieval in 949) [ClassicSimilarity], result of:
      0.012401459 = score(doc=949,freq=2.0), product of:
        0.07421378 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02453417 = queryNorm
        0.16710453 = fieldWeight in 949, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=949)
  0.14285715 = coord(2/14)

Abstract

Purpose This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015. Design/methodology/approach The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology. Findings The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed. Originality/value Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

0.0025213675 = product of:
  0.017649572 = sum of:
    0.009339468 = weight(_text_:information in 998) [ClassicSimilarity], result of:
      0.009339468 = score(doc=998,freq=10.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.21684799 = fieldWeight in 998, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=998)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 998) [ClassicSimilarity], result of:
          0.016620208 = score(doc=998,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 998, 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=998)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Abstract

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production.

Date

22. 6.2023 18:15:06

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.811-827

0.0025173177 = product of:
  0.017621223 = sum of:
    0.013444485 = weight(_text_:system in 545) [ClassicSimilarity], result of:
      0.013444485 = score(doc=545,freq=2.0), product of:
        0.07727166 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.02453417 = queryNorm
        0.17398985 = fieldWeight in 545, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=545)
    0.004176737 = weight(_text_:information in 545) [ClassicSimilarity], result of:
      0.004176737 = score(doc=545,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.09697737 = fieldWeight in 545, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=545)
  0.14285715 = coord(2/14)

Abstract

The emphasis on research evaluation has brought scrutiny to the role of self-citations in the scholarly communication process. While author self-citations have been studied at length, little is known on national-level self-references (SRs). This paper analyses the citation context of national SRs, using the full-text of 184,859 papers published in PLOS journals. It investigates the differences between national SRs and nonself-references (NSRs) in terms of their in-text mention, presence in enumerations, and location features. For all countries, national SRs exhibit a higher level of engagement than NSRs. NSRs are more often found in enumerative citances than SRs, which suggests that researchers pay more attention to domestic than foreign studies. There are more mentions of national research in the methods section, which provides evidence that methodologies developed in a nation are more likely to be used by other researchers from the same nation. Publications from the United States are cited at a higher rate in each of the sections, indicating that the country still maintains a dominant position in science. On the whole, this paper contributes to a better understanding of the role of national SRs in the scholarly communication system, and how it varies across countries and over time.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.5, S.671-686

0.0023805113 = product of:
  0.016663577 = sum of:
    0.008353474 = weight(_text_:information in 178) [ClassicSimilarity], result of:
      0.008353474 = score(doc=178,freq=8.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.19395474 = fieldWeight in 178, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=178)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 178) [ClassicSimilarity], result of:
          0.016620208 = score(doc=178,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 178, 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=178)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Abstract

Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 72(2020) no.6, S.945-962

0.0021406014 = product of:
  0.014984209 = sum of:
    0.0050120843 = weight(_text_:information in 182) [ClassicSimilarity], result of:
      0.0050120843 = score(doc=182,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.116372846 = fieldWeight in 182, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=182)
    0.009972124 = product of:
      0.019944249 = sum of:
        0.019944249 = weight(_text_:22 in 182) [ClassicSimilarity], result of:
          0.019944249 = score(doc=182,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.23214069 = fieldWeight in 182, 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=182)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 73(2021) no.1, S.129-143

0.0021406014 = product of:
  0.014984209 = sum of:
    0.0050120843 = weight(_text_:information in 918) [ClassicSimilarity], result of:
      0.0050120843 = score(doc=918,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.116372846 = fieldWeight in 918, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=918)
    0.009972124 = product of:
      0.019944249 = sum of:
        0.019944249 = weight(_text_:22 in 918) [ClassicSimilarity], result of:
          0.019944249 = score(doc=918,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.23214069 = fieldWeight in 918, 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=918)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

21. 3.2023 19:22:14

Source

Journal of the Association for Information Science and Technology. 74(2023) no.4, S.415-433

0.0020923724 = product of:
  0.014646606 = sum of:
    0.0047254385 = weight(_text_:information in 608) [ClassicSimilarity], result of:
      0.0047254385 = score(doc=608,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.10971737 = fieldWeight in 608, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=608)
    0.009921167 = weight(_text_:retrieval in 608) [ClassicSimilarity], result of:
      0.009921167 = score(doc=608,freq=2.0), product of:
        0.07421378 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02453417 = queryNorm
        0.13368362 = fieldWeight in 608, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=608)
  0.14285715 = coord(2/14)

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.

0.0020309861 = product of:
  0.014216902 = sum of:
    0.005906798 = weight(_text_:information in 994) [ClassicSimilarity], result of:
      0.005906798 = score(doc=994,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.13714671 = fieldWeight in 994, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=994)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 994) [ClassicSimilarity], result of:
          0.016620208 = score(doc=994,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 994, 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=994)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Abstract

Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Date

22. 6.2023 18:07:12

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790

0.0017838344 = product of:
  0.012486841 = sum of:
    0.004176737 = weight(_text_:information in 882) [ClassicSimilarity], result of:
      0.004176737 = score(doc=882,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.09697737 = fieldWeight in 882, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=882)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 882) [ClassicSimilarity], result of:
          0.016620208 = score(doc=882,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = 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)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

22. 1.2023 18:37:33

Source

Journal of the Association for Information Science and Technology. 74(2023) no.2, S.150-167

0.0017838344 = product of:
  0.012486841 = sum of:
    0.004176737 = weight(_text_:information in 916) [ClassicSimilarity], result of:
      0.004176737 = score(doc=916,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.09697737 = fieldWeight in 916, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=916)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 916) [ClassicSimilarity], result of:
          0.016620208 = score(doc=916,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 916, 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=916)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

21. 3.2023 19:22:02

Source

Journal of the Association for Information Science and Technology. 74(2023) no.4, S.402-414

0.0017838344 = product of:
  0.012486841 = sum of:
    0.004176737 = weight(_text_:information in 992) [ClassicSimilarity], result of:
      0.004176737 = score(doc=992,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.09697737 = fieldWeight in 992, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=992)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 992) [ClassicSimilarity], result of:
          0.016620208 = score(doc=992,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 992, 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=992)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

22. 6.2023 14:09:06

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.745-758

0.0017838344 = product of:
  0.012486841 = sum of:
    0.004176737 = weight(_text_:information in 995) [ClassicSimilarity], result of:
      0.004176737 = score(doc=995,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.09697737 = fieldWeight in 995, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=995)
    0.008310104 = product of:
      0.016620208 = sum of:
        0.016620208 = weight(_text_:22 in 995) [ClassicSimilarity], result of:
          0.016620208 = score(doc=995,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.19345059 = fieldWeight in 995, 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=995)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Date

22. 6.2023 18:11:50

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.791-810

0.0013865514 = product of:
  0.019411718 = sum of:
    0.019411718 = weight(_text_:information in 153) [ClassicSimilarity], result of:
      0.019411718 = score(doc=153,freq=30.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.4507101 = fieldWeight in 153, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=153)
  0.071428575 = coord(1/14)

Abstract

Information grounds emerge as people share information with others in a common place. Many studies have investigated the emergence of information grounds in public places. This study pays attention to the emergence, peak, and abeyance of an online information ground. It investigates a Facebook group used by youth for sharing information when misinformation spread wildly during the 2011 violence in Ambon, Indonesia. The findings demonstrate change and continuity in an online information ground; it became an information hub when reaching a peak cycle, and an information repository when entering into abeyance. Despite this period of nonactivity, the friendships and collective memories resulting from information ground interactions last over time and can be used for reactivating the online information ground when new needs emerge. Illuminating the lifecycles of an online information ground, the findings have potential to explain the dynamic of users' interactions with others and with information in quotidian spaces.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.3, S.302-314

5.06297E-4 = product of:
  0.0070881573 = sum of:
    0.0070881573 = weight(_text_:information in 5676) [ClassicSimilarity], result of:
      0.0070881573 = score(doc=5676,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.16457605 = fieldWeight in 5676, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=5676)
  0.071428575 = coord(1/14)

Abstract

This article offers an adjustment to the recently developed Euclidean Index (Perry and Reny, 2016). The proposed companion metric reflects the impact of the journal in which an article appears; the rationale for incorporating this information is to reflect higher costs of production and higher review standards, and to mitigate the heavily truncated citation counts that often arise in promotion, renewal, and tenure deliberations. Additionally, focusing jointly on citations and journal impact diversifies the assessment process, and can thereby help avoid misjudging scholars with modest citation counts in high-level journals. A combination of both metrics is also proposed, which nests each as a special case. The approach is demonstrated using a generic journal ranking metric, but can be adapted to most any stated or revealed preference measure of journal impact.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.3, S.370-373