Search (139 results, page 1 of 7)

0.16626924 = product of:
  0.4988077 = sum of:
    0.24940385 = weight(_text_:2f in 2188) [ClassicSimilarity], result of:
      0.24940385 = score(doc=2188,freq=2.0), product of:
        0.33282363 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.039257277 = queryNorm
        0.7493574 = fieldWeight in 2188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.0625 = fieldNorm(doc=2188)
    0.24940385 = weight(_text_:2f in 2188) [ClassicSimilarity], result of:
      0.24940385 = score(doc=2188,freq=2.0), product of:
        0.33282363 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.039257277 = queryNorm
        0.7493574 = fieldWeight in 2188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.0625 = fieldNorm(doc=2188)
  0.33333334 = coord(2/6)

Content

Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.

0.055006143 = product of:
  0.16501842 = sum of:
    0.15172137 = weight(_text_:politics in 178) [ClassicSimilarity], result of:
      0.15172137 = score(doc=178,freq=4.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.5494881 = fieldWeight in 178, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.0390625 = fieldNorm(doc=178)
    0.013297048 = product of:
      0.026594097 = sum of:
        0.026594097 = weight(_text_:22 in 178) [ClassicSimilarity], result of:
          0.026594097 = score(doc=178,freq=2.0), product of:
            0.1374723 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039257277 = 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)
  0.33333334 = coord(2/6)

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

0.043554243 = product of:
  0.26132545 = sum of:
    0.26132545 = weight(_text_:1989 in 2828) [ClassicSimilarity], result of:
      0.26132545 = score(doc=2828,freq=5.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        1.3640437 = fieldWeight in 2828, product of:
          2.236068 = tf(freq=5.0), with freq of:
            5.0 = termFreq=5.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.125 = fieldNorm(doc=2828)
  0.16666667 = coord(1/6)

Source

Scientometrics. 15(1989), S.437-447

Year

1989

0.043554243 = product of:
  0.26132545 = sum of:
    0.26132545 = weight(_text_:1989 in 341) [ClassicSimilarity], result of:
      0.26132545 = score(doc=341,freq=5.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        1.3640437 = fieldWeight in 341, product of:
          2.236068 = tf(freq=5.0), with freq of:
            5.0 = termFreq=5.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.125 = fieldNorm(doc=341)
  0.16666667 = coord(1/6)

Source

Annual review of information science and technology. 24(1989), S.119-186

Year

1989

0.025978412 = product of:
  0.07793523 = sum of:
    0.061978772 = weight(_text_:1989 in 402) [ClassicSimilarity], result of:
      0.061978772 = score(doc=402,freq=2.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.32351136 = fieldWeight in 402, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.046875 = fieldNorm(doc=402)
    0.015956458 = product of:
      0.031912915 = sum of:
        0.031912915 = weight(_text_:22 in 402) [ClassicSimilarity], result of:
          0.031912915 = score(doc=402,freq=2.0), product of:
            0.1374723 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039257277 = queryNorm
            0.23214069 = fieldWeight in 402, 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=402)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Date

22. 7.2012 19:35:26

Footnote

Besteht aus 4 Teilen: Teil 1: Eden, D., A. Arndt, A. Hoffer, T. Raschke u. P. Schön: Die Nachrichten für Dokumentation in den Jahren 1950 bis 1962 (S.159-163). Teil 2: Brose, M., E. durst, D. Nitzsche, D. Veckenstedt u. R. Wein: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1963-1975 (S.164-170). Teil 3: Bösel, J., G. Ebert, P. Garz,, M. Iwanow u. B. Russ: Methoden und Ergebnisse einer statistischen Auswertung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1976 bis 1988 (S.171-174). Teil 4: Engelage, H., S. Jansen, R. Mertins, K. Redel u. S. Ring: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) / "Information. Wissenschaft & Praxis" (IWP) 1989-2011 (S.164-170).

0.02530273 = product of:
  0.15181638 = sum of:
    0.15181638 = weight(_text_:1989 in 5069) [ClassicSimilarity], result of:
      0.15181638 = score(doc=5069,freq=3.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.7924378 = fieldWeight in 5069, product of:
          1.7320508 = tf(freq=3.0), with freq of:
            3.0 = termFreq=3.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.09375 = fieldNorm(doc=5069)
  0.16666667 = coord(1/6)

Year

1989

0.021777121 = product of:
  0.13066272 = sum of:
    0.13066272 = weight(_text_:1989 in 5059) [ClassicSimilarity], result of:
      0.13066272 = score(doc=5059,freq=5.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.68202186 = fieldWeight in 5059, product of:
          2.236068 = tf(freq=5.0), with freq of:
            5.0 = termFreq=5.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.0625 = fieldNorm(doc=5059)
  0.16666667 = coord(1/6)

Source

Information processing and management. 25(1989) no.6, S.677-691

Year

1989

0.019054981 = product of:
  0.11432988 = sum of:
    0.11432988 = weight(_text_:1989 in 5060) [ClassicSimilarity], result of:
      0.11432988 = score(doc=5060,freq=5.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.5967691 = fieldWeight in 5060, product of:
          2.236068 = tf(freq=5.0), with freq of:
            5.0 = termFreq=5.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5060)
  0.16666667 = coord(1/6)

Source

Indexer. 17(1989), S.234-238

Year

1989

0.018738247 = product of:
  0.112429485 = sum of:
    0.112429485 = product of:
      0.22485897 = sum of:
        0.22485897 = weight(_text_:forecasting in 1452) [ClassicSimilarity], result of:
          0.22485897 = score(doc=1452,freq=2.0), product of:
            0.33784202 = queryWeight, product of:
              8.6058445 = idf(docFreq=21, maxDocs=44218)
              0.039257277 = queryNorm
            0.6655743 = fieldWeight in 1452, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.6058445 = idf(docFreq=21, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1452)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Abstract

Research fronts, defined as clusters of documents that tend to cite a fixed, time invariant set of base documents, are plotted as time lines for visualization and exploration. Using a set of documents related to the subject of anthrax research, this article illustrates the construction, exploration, and interpretation of time lines for the purpose of identifying and visualizing temporal changes in research activity through journal articles. Such information is useful for presentation to meinbers of expert panels used for technology forecasting.

0.017880535 = product of:
  0.107283205 = sum of:
    0.107283205 = weight(_text_:politics in 4920) [ClassicSimilarity], result of:
      0.107283205 = score(doc=4920,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.38854676 = fieldWeight in 4920, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4920)
  0.16666667 = coord(1/6)

Abstract

Citation indictors are increasingly used in some subject areas to support peer review in the evaluation of researchers and departments. Nevertheless, traditional journal-based citation indexes may be inadequate for the citation impact assessment of book-based disciplines. This article examines whether online citations from Google Books and Google Scholar can provide alternative sources of citation evidence. To investigate this, we compared the citation counts to 1,000 books submitted to the 2008 U.K. Research Assessment Exercise (RAE) from Google Books and Google Scholar with Scopus citations across seven book-based disciplines (archaeology; law; politics and international studies; philosophy; sociology; history; and communication, cultural, and media studies). Google Books and Google Scholar citations to books were 1.4 and 3.2 times more common than were Scopus citations, and their medians were more than twice and three times as high as were Scopus median citations, respectively. This large number of citations is evidence that in book-oriented disciplines in the social sciences, arts, and humanities, online book citations may be sufficiently numerous to support peer review for research evaluation, at least in the United Kingdom.

0.017880535 = product of:
  0.107283205 = sum of:
    0.107283205 = weight(_text_:politics in 382) [ClassicSimilarity], result of:
      0.107283205 = score(doc=382,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.38854676 = fieldWeight in 382, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.0390625 = fieldNorm(doc=382)
  0.16666667 = coord(1/6)

Abstract

Although there is some evidence that online videos are increasingly used by academics for informal scholarly communication and teaching, the extent to which they are used in published academic research is unknown. This article explores the extent to which YouTube videos are cited in academic publications and whether there are significant broad disciplinary differences in this practice. To investigate, we extracted the URL citations to YouTube videos from academic publications indexed by Scopus. A total of 1,808 Scopus publications cited at least one YouTube video, and there was a steady upward growth in citing online videos within scholarly publications from 2006 to 2011, with YouTube citations being most common within arts and humanities (0.3%) and the social sciences (0.2%). A content analysis of 551 YouTube videos cited by research articles indicated that in science (78%) and in medicine and health sciences (77%), over three fourths of the cited videos had either direct scientific (e.g., laboratory experiments) or scientific-related contents (e.g., academic lectures or education) whereas in the arts and humanities, about 80% of the YouTube videos had art, culture, or history themes, and in the social sciences, about 63% of the videos were related to news, politics, advertisements, and documentaries. This shows both the disciplinary differences and the wide variety of innovative research communication uses found for videos within the different subject areas.

0.017880535 = product of:
  0.107283205 = sum of:
    0.107283205 = weight(_text_:politics in 3760) [ClassicSimilarity], result of:
      0.107283205 = score(doc=3760,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.38854676 = fieldWeight in 3760, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3760)
  0.16666667 = coord(1/6)

Abstract

Although news stories target the general public and are sometimes inaccurate, they can serve as sources of real-world information for researchers. This article investigates the extent to which academics exploit journalism using content and citation analyses of online BBC News stories cited by Scopus articles. A total of 27,234 Scopus-indexed publications have cited at least one BBC News story, with a steady annual increase. Citations from the arts and humanities (2.8% of publications in 2015) and social sciences (1.5%) were more likely than citations from medicine (0.1%) and science (<0.1%). Surprisingly, half of the sampled Scopus-cited science and technology (53%) and medicine and health (47%) stories were based on academic research, rather than otherwise unpublished information, suggesting that researchers have chosen a lower-quality secondary source for their citations. Nevertheless, the BBC News stories that were most frequently cited by Scopus, Google Books, and Wikipedia introduced new information from many different topics, including politics, business, economics, statistics, and reports about events. Thus, news stories are mediating real-world knowledge into the academic domain, a potential cause for concern.

0.017880535 = product of:
  0.107283205 = sum of:
    0.107283205 = weight(_text_:politics in 4256) [ClassicSimilarity], result of:
      0.107283205 = score(doc=4256,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.38854676 = fieldWeight in 4256, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4256)
  0.16666667 = coord(1/6)

Abstract

Numerous web co-link studies have analyzed a wide variety of websites ranging from those in the academic and business arena to those dealing with politics and governments. Such studies uncover rich information about these organizations. In recent years, however, there has been a dearth of co-link analysis, mainly due to the lack of sources from which co-link data can be collected directly. Although several commercial services such as Alexa provide inlink data, none provide co-link data. We propose a new approach to web co-link analysis that can alleviate this problem so that researchers can continue to mine the valuable information contained in co-link data. The proposed approach has two components: (a) generating co-link data from inlink data using a computer program; (b) analyzing co-link data at the site level in addition to the page level that previous co-link analyses have used. The site-level analysis has the potential of expanding co-link data sources. We tested this proposed approach by analyzing a group of websites focused on vaccination using Moz inlink data. We found that the approach is feasible, as we were able to generate co-link data from inlink data and analyze the co-link data with multidimensional scaling.

0.014608538 = product of:
  0.08765122 = sum of:
    0.08765122 = weight(_text_:1989 in 4291) [ClassicSimilarity], result of:
      0.08765122 = score(doc=4291,freq=4.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.45751417 = fieldWeight in 4291, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.046875 = fieldNorm(doc=4291)
  0.16666667 = coord(1/6)

Abstract

Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990-2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments.

0.014304428 = product of:
  0.08582657 = sum of:
    0.08582657 = weight(_text_:politics in 3312) [ClassicSimilarity], result of:
      0.08582657 = score(doc=3312,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.31083742 = fieldWeight in 3312, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.03125 = fieldNorm(doc=3312)
  0.16666667 = coord(1/6)

Abstract

If you are an academic researcher but did not earn (yet) your Nobel prize or your retirement, it is unlikely you never heard about research metrics. These metrics aim at quantifying various aspects of the research process, at the level of individual researchers (e.g. h-index, altmetrics), scientific journals (e.g. impact factors) or entire universities/ countries (e.g. rankings). Although such "measurements" have existed in a simple form for a long time, their widespread calculation was enabled by the advent of the digital era (large amount of data available worldwide in a computer-compatible format). And in this new era, what becomes technically possible will be done, and what is done and appears to simplify our lives will be used. As a result, a rapidly growing number of statistics-based numerical indices are nowadays fed into decisionmaking processes. This is true in nearly all aspects of society (politics, economy, education and private life), and in particular in research, where metrics play an increasingly important role in determining positions, funding, awards, research programs, career choices, reputations, etc.

0.014304428 = product of:
  0.08582657 = sum of:
    0.08582657 = weight(_text_:politics in 3805) [ClassicSimilarity], result of:
      0.08582657 = score(doc=3805,freq=2.0), product of:
        0.27611402 = queryWeight, product of:
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.039257277 = queryNorm
        0.31083742 = fieldWeight in 3805, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.033448 = idf(docFreq=105, maxDocs=44218)
          0.03125 = fieldNorm(doc=3805)
  0.16666667 = coord(1/6)

Abstract

The research evaluation market is booming. "Ranking," "metrics," "h-index," and "impact factors" are reigning buzzwords. Government and research administrators want to evaluate everything -- teachers, professors, training programs, universities -- using quantitative indicators. Among the tools used to measure "research excellence," bibliometrics -- aggregate data on publications and citations -- has become dominant. Bibliometrics is hailed as an "objective" measure of research quality, a quantitative measure more useful than "subjective" and intuitive evaluation methods such as peer review that have been used since scientific papers were first published in the seventeenth century. In this book, Yves Gingras offers a spirited argument against an unquestioning reliance on bibliometrics as an indicator of research quality. Gingras shows that bibliometric rankings have no real scientific validity, rarely measuring what they pretend to. Although the study of publication and citation patterns, at the proper scales, can yield insights on the global dynamics of science over time, ill-defined quantitative indicators often generate perverse and unintended effects on the direction of research. Moreover, abuse of bibliometrics occurs when data is manipulated to boost rankings. Gingras looks at the politics of evaluation and argues that using numbers can be a way to control scientists and diminish their autonomy in the evaluation process. Proposing precise criteria for establishing the validity of indicators at a given scale of analysis, Gingras questions why universities are so eager to let invalid indicators influence their research strategy.

0.013384463 = product of:
  0.080306776 = sum of:
    0.080306776 = product of:
      0.16061355 = sum of:
        0.16061355 = weight(_text_:forecasting in 4445) [ClassicSimilarity], result of:
          0.16061355 = score(doc=4445,freq=2.0), product of:
            0.33784202 = queryWeight, product of:
              8.6058445 = idf(docFreq=21, maxDocs=44218)
              0.039257277 = queryNorm
            0.47541022 = fieldWeight in 4445, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.6058445 = idf(docFreq=21, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4445)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Abstract

Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.

0.012051429 = product of:
  0.07230857 = sum of:
    0.07230857 = weight(_text_:1989 in 761) [ClassicSimilarity], result of:
      0.07230857 = score(doc=761,freq=2.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.37742993 = fieldWeight in 761, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.0546875 = fieldNorm(doc=761)
  0.16666667 = coord(1/6)

0.010329796 = product of:
  0.061978772 = sum of:
    0.061978772 = weight(_text_:1989 in 355) [ClassicSimilarity], result of:
      0.061978772 = score(doc=355,freq=2.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.32351136 = fieldWeight in 355, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.046875 = fieldNorm(doc=355)
  0.16666667 = coord(1/6)

Abstract

Presents a quantitative anaylsis of 100 references retrieved on the information use and needs of humanities researchers, consisting of journal articles (57%), dissertations (26%), conference proceedings (11%) and books (6%). 3 journal titles were found to contribute more than a third of the articles on this subject. About 88% (23) of the dissertations are doctoral theses submitted to universities in the USA. about 51% of the retrieved items were published between 1980-1989 which form the peak of studies in this area and the situation stabilizes to an average of about 2 studies a year in the post 1990 years. The bulk of the studies is about characteirstics of information sources used and of these citation studies are predominant. Information needs and use in the field of literature, history and music (62%, 41) constitute the majority of studies in this area. The review studies come under 3 categories; the library and humanities scholars; research and information seeking behaviour of the humanities researchers and the characteristics of sources used

0.010329796 = product of:
  0.061978772 = sum of:
    0.061978772 = weight(_text_:1989 in 1376) [ClassicSimilarity], result of:
      0.061978772 = score(doc=1376,freq=2.0), product of:
        0.19158144 = queryWeight, product of:
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.039257277 = queryNorm
        0.32351136 = fieldWeight in 1376, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8801513 = idf(docFreq=912, maxDocs=44218)
          0.046875 = fieldNorm(doc=1376)
  0.16666667 = coord(1/6)

Abstract

This article examines the impact of the Internet and the age distribution of research scholars on academic citation age with a mathematical model proposed by Barnett, Fink, and Debus (1989) and a revised model that incorporates information about the online environment and scholar age distribution. The modified model fits the data well, accounting for 99.6% of the variance for science citations and 99.8% for social science citations. The Internet's impact on the aging process of academic citations has been very small, accounting for only 0.1% for the social sciences and 0.8% for the sciences. Rather than resulting in the use of more recent citations, the Internet appears to have lengthened the average life of academic citations by 6 to 8 months. The aging of scholars seems to have a greater impact, accounting for 2.8% of the variance for the sciences and 0.9% for the social sciences. However, because the diffusion of the Internet and the aging of the professoriate are correlated over this time period, differentiating their effects is somewhat problematic.