Search (10 results, page 1 of 1)

0.06484437 = product of:
  0.12968874 = sum of:
    0.0864512 = weight(_text_:fields in 995) [ClassicSimilarity], result of:
      0.0864512 = score(doc=995,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 995, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=995)
    0.04323754 = weight(_text_:22 in 995) [ClassicSimilarity], result of:
      0.04323754 = score(doc=995,freq=2.0), product of:
        0.2235069 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.06382575 = 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(2/4)

Abstract

Collaboration is encouraged because it is believed to improve academic research, supported by indirect evidence in the form of more coauthored articles being more cited. Nevertheless, this might not reflect quality but increased self-citations or the "audience effect": citations from increased awareness through multiple author networks. We address this with the first science wide investigation into whether author numbers associate with journal article quality, using expert peer quality judgments for 122,331 articles from the 2014-20 UK national assessment. Spearman correlations between author numbers and quality scores show moderately strong positive associations (0.2-0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering and social sciences, with weak negative/positive or no associations in various arts and humanities, and a possible negative association for decision sciences. This gives the first systematic evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other nonquality factors account for the higher citation rates of coauthored articles in some fields.

Date

22. 6.2023 18:11:50

0.0432256 = product of:
  0.1729024 = sum of:
    0.1729024 = weight(_text_:fields in 1033) [ClassicSimilarity], result of:
      0.1729024 = score(doc=1033,freq=8.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.54708534 = fieldWeight in 1033, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1033)
  0.25 = coord(1/4)

Abstract

Citation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large-scale science-wide academic evaluation of the relationship between research quality and citations (field normalized citation counts), correlating them for 87,739 journal articles in 34 field-based UK Units of Assessment (UoA). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, while appropriately field normalized citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.

0.030565115 = product of:
  0.12226046 = sum of:
    0.12226046 = weight(_text_:fields in 3317) [ClassicSimilarity], result of:
      0.12226046 = score(doc=3317,freq=4.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.38684773 = fieldWeight in 3317, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3317)
  0.25 = coord(1/4)

Abstract

Citations from patents to scientific publications provide useful evidence about the commercial impact of academic research, but automatically searchable databases are needed to exploit this connection for large-scale patent citation evaluations. Google covers multiple different international patent office databases but does not index patent citations or allow automatic searches. In response, this article introduces a semiautomatic indirect method via Bing to extract and filter patent citations from Google to academic papers with an overall precision of 98%. The method was evaluated with 322,192 science and engineering Scopus articles from every second year for the period 1996-2012. Although manual Google Patent searches give more results, especially for articles with many patent citations, the difference is not large enough to be a major problem. Within Biomedical Engineering, Biotechnology, and Pharmacology & Pharmaceutics, 7% to 10% of Scopus articles had at least one patent citation but other fields had far fewer, so patent citation analysis is only relevant for a minority of publications. Low but positive correlations between Google Patent citations and Scopus citations across all fields suggest that traditional citation counts cannot substitute for patent citations when evaluating research.

0.0216128 = product of:
  0.0864512 = sum of:
    0.0864512 = weight(_text_:fields in 3226) [ClassicSimilarity], result of:
      0.0864512 = score(doc=3226,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 3226, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3226)
  0.25 = coord(1/4)

Abstract

Scholars writing books that are widely used to support teaching in higher education may be undervalued because of a lack of evidence of teaching value. Although sales data may give credible evidence for textbooks, these data may poorly reflect educational uses of other types of books. As an alternative, this article proposes a method to search automatically for mentions of books in online academic course syllabi based on Bing searches for syllabi mentioning a given book, filtering out false matches through an extensive set of rules. The method had an accuracy of over 90% based on manual checks of a sample of 2,600 results from the initial Bing searches. Over one third of about 14,000 monographs checked had one or more academic syllabus mention, with more in the arts and humanities (56%) and social sciences (52%). Low but significant correlations between syllabus mentions and citations across most fields, except the social sciences, suggest that books tend to have different levels of impact for teaching and research. In conclusion, the automatic syllabus search method gives a new way to estimate the educational utility of books in a way that sales data and citation counts cannot.

0.0216128 = product of:
  0.0864512 = sum of:
    0.0864512 = weight(_text_:fields in 3349) [ClassicSimilarity], result of:
      0.0864512 = score(doc=3349,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 3349, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3349)
  0.25 = coord(1/4)

Abstract

The large multidisciplinary academic social website ResearchGate aims to help academics to connect with each other and to publicize their work. Despite its popularity, little is known about the age and discipline of the articles uploaded and viewed in the site and whether publication statistics from the site could be useful impact indicators. In response, this article assesses samples of ResearchGate articles uploaded at specific dates, comparing their views in the site to their Mendeley readers and Scopus-indexed citations. This analysis shows that ResearchGate is dominated by recent articles, which attract about three times as many views as older articles. ResearchGate has uneven coverage of scholarship, with the arts and humanities, health professions, and decision sciences poorly represented and some fields receiving twice as many views per article as others. View counts for uploaded articles have low to moderate positive correlations with both Scopus citations and Mendeley readers, which is consistent with them tending to reflect a wider audience than Scopus-publishing scholars. Hence, for articles uploaded to the site, view counts may give a genuinely new audience indicator.

0.0216128 = product of:
  0.0864512 = sum of:
    0.0864512 = weight(_text_:fields in 3440) [ClassicSimilarity], result of:
      0.0864512 = score(doc=3440,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 3440, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3440)
  0.25 = coord(1/4)

Abstract

Individual academics and research evaluators often need to assess the value of published research. Although citation counts are a recognized indicator of scholarly impact, alternative data is needed to provide evidence of other types of impact, including within education and wider society. Wikipedia is a logical choice for both of these because the role of a general encyclopaedia is to be an understandable repository of facts about a diverse array of topics and hence it may cite research to support its claims. To test whether Wikipedia could provide new evidence about the impact of scholarly research, this article counted citations to 302,328 articles and 18,735 monographs in English indexed by Scopus in the period 2005 to 2012. The results show that citations from Wikipedia to articles are too rare for most research evaluation purposes, with only 5% of articles being cited in all fields. In contrast, a third of monographs have at least one citation from Wikipedia, with the most in the arts and humanities. Hence, Wikipedia citations can provide extra impact evidence for academic monographs. Nevertheless, the results may be relatively easily manipulated and so Wikipedia is not recommended for evaluations affecting stakeholder interests.

0.0216128 = product of:
  0.0864512 = sum of:
    0.0864512 = weight(_text_:fields in 3768) [ClassicSimilarity], result of:
      0.0864512 = score(doc=3768,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 3768, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3768)
  0.25 = coord(1/4)

Abstract

Although peer-review and citation counts are commonly used to help assess the scholarly impact of published research, informal reader feedback might also be exploited to help assess the wider impacts of books, such as their educational or cultural value. The social website Goodreads seems to be a reasonable source for this purpose because it includes a large number of book reviews and ratings by many users inside and outside of academia. To check this, Goodreads book metrics were compared with different book-based impact indicators for 15,928 academic books across broad fields. Goodreads engagements were numerous enough in the arts (85% of books had at least one), humanities (80%), and social sciences (67%) for use as a source of impact evidence. Low and moderate correlations between Goodreads book metrics and scholarly or non-scholarly indicators suggest that reader feedback in Goodreads reflects the many purposes of books rather than a single type of impact. Although Goodreads book metrics can be manipulated, they could be used guardedly by academics, authors, and publishers in evaluations.

0.0216128 = product of:
  0.0864512 = sum of:
    0.0864512 = weight(_text_:fields in 947) [ClassicSimilarity], result of:
      0.0864512 = score(doc=947,freq=2.0), product of:
        0.31604284 = queryWeight, product of:
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.06382575 = queryNorm
        0.27354267 = fieldWeight in 947, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.951651 = idf(docFreq=849, maxDocs=44218)
          0.0390625 = fieldNorm(doc=947)
  0.25 = coord(1/4)

Abstract

Altmetrics are web-based quantitative impact or attention indicators for academic articles that have been proposed to supplement citation counts. This article reports the first assessment of the extent to which mature altmetrics from Altmetric.com and Mendeley associate with individual article quality scores. It exploits expert norm-referenced peer review scores from the UK Research Excellence Framework 2021 for 67,030+ journal articles in all fields 2014-2017/2018, split into 34 broadly field-based Units of Assessment (UoAs). Altmetrics correlated more strongly with research quality than previously found, although less strongly than raw and field normalized Scopus citation counts. Surprisingly, field normalizing citation counts can reduce their strength as a quality indicator for articles in a single field. For most UoAs, Mendeley reader counts are the best altmetric (e.g., three Spearman correlations with quality scores above 0.5), tweet counts are also a moderate strength indicator in eight UoAs (Spearman correlations with quality scores above 0.3), ahead of news (eight correlations above 0.3, but generally weaker), blogs (five correlations above 0.3), and Facebook (three correlations above 0.3) citations, at least in the United Kingdom. In general, altmetrics are the strongest indicators of research quality in the health and physical sciences and weakest in the arts and humanities.

0.010809385 = product of:
  0.04323754 = sum of:
    0.04323754 = weight(_text_:22 in 586) [ClassicSimilarity], result of:
      0.04323754 = score(doc=586,freq=2.0), product of:
        0.2235069 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.06382575 = queryNorm
        0.19345059 = fieldWeight in 586, 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=586)
  0.25 = coord(1/4)

Abstract

Although the analysis of citations in the scholarly literature is now an established and relatively well understood part of information science, not enough is known about citations that can be found on the Web. In particular, are there new Web types, and if so, are these trivial or potentially useful for studying or evaluating research communication? We sought evidence based upon a sample of 1,577 Web citations of the URLs or titles of research articles in 64 open-access journals from biology, physics, chemistry, and computing. Only 25% represented intellectual impact, from references of Web documents (23%) and other informal scholarly sources (2%). Many of the Web/URL citations were created for general or subject-specific navigation (45%) or for self-publicity (22%). Additional analyses revealed significant disciplinary differences in the types of Google unique Web/URL citations as well as some characteristics of scientific open-access publishing on the Web. We conclude that the Web provides access to a new and different type of citation information, one that may therefore enable us to measure different aspects of research, and the research process in particular; but to obtain good information, the different types should be separated.

0.010809385 = product of:
  0.04323754 = sum of:
    0.04323754 = weight(_text_:22 in 2593) [ClassicSimilarity], result of:
      0.04323754 = score(doc=2593,freq=2.0), product of:
        0.2235069 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.06382575 = queryNorm
        0.19345059 = fieldWeight in 2593, 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=2593)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22