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  • 1.
    Ahlgren, Per
    et al.
    KTH, School of Education and Communication in Engineering Science (ECE).
    Colliander, C.
    Sjögårde, Peter
    KTH, School of Education and Communication in Engineering Science (ECE). Department of ALM, Uppsala University, Uppsala, Sweden.
    Exploring the relation between referencing practices and citation impact: A large-scale study based on Web of Science data2018In: Journal of the Association for Information Science and Technology, ISSN 2330-1635, E-ISSN 2330-1643, Vol. 69, no 5, p. 728-743Article in journal (Refereed)
    Abstract [en]

    In this large-scale contribution, we deal with the relationship between properties of cited references of Web of Science articles and the field normalized citation rate of these articles. Using nearly 1 million articles, and three classification systems with different levels of granularity, we study the effects of number of cited references, share of references covered by Web of Science, mean age of references and mean citation rate of references on field normalized citation rate. To expose the relationship between the predictor variables and the response variable, we use quantile regression. We found that a higher number of references, a higher share of references to publications within Web of Science and references to more recent publications correlate with citation impact. A correlation was observed even when normalization was done with a finely grained classification system. The predictor variables affected citation impact to a larger extent at higher quantile levels. Regarding the relative importance of the predictor variables, citation impact of the cited references was in general the least important variable. Number of cited references carried most of the importance for both low and medium quantile levels, but this importance was lessened at the highest considered level.

  • 2.
    Wang, Qi
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.). Danish Centre for Studies in Research & Research Policy, Aarhus University, Denmark.
    A Bibliometric Model for Identifying Emerging Research Topics2018In: Journal of the Association for Information Science and Technology, ISSN 2330-1635, E-ISSN 2330-1643, Vol. 69, no 2, p. 290-304Article in journal (Refereed)
    Abstract [en]

    Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However, as indicated by Rotolo et al. (Research Policy 44, 1827-1843, ), the most serious problems are the lack of an acknowledged definition of emergence and incomplete elaboration of the linkages between the definitions that are used and the indicators that are created. With these issues in mind, this study first adjusts the definition of an emerging technology that Rotolo et al. (2015) have proposed to accommodate the analysis. Next, a set of criteria for the identification of emerging topics is proposed according to the adjusted definition and attributes of emergence. Using two sets of parameter values, several emerging research topics are identified. Finally, evaluation tests are conducted by demonstration of the proposed approach and comparison with previous studies. The strength of the present methodology lies in the fact that it is fully transparent, straightforward, and flexible.

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