Search (135 results, page 7 of 7)

  • × language_ss:"e"
  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Huang, M.-H.; Lin, C.-S.; Chen, D.-Z.: Counting methods, country rank changes, and counting inflation in the assessment of national research productivity and impact (2011) 0.00 
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  2. Liu, J.S.; Lu, L.Y.Y.: ¬An integrated approach for main path analysis : development of the Hirsch index as an example (2012) 0.00 
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    Object
    h-index
  3. Zhao, D.; Strotmann, A.: ¬The knowledge base and research front of information science 2006-2010 : an author cocitation and bibliographic coupling analysis (2014) 0.00 
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    Abstract
    This study continues a long history of author cocitation analysis (and more recently, author bibliographic coupling analysis) of the intellectual structure of information science (IS) into the time period 2006 to 2010 (IS 2006-2010). We find that web technologies continue to drive developments, especially at the research front, although perhaps more indirectly than before. A broadening of perspectives is visible in IS 2006-2010, where network science becomes influential and where full-text analysis methods complement traditional computer science influences. Research in the areas of the h-index and mapping of science appears to have been highlights of IS 2006-2011. This study tests and confirms a forecast made previously by comparing knowledge-base and research-front findings for IS 2001-2005, which expected both the information retrieval (IR) systems and webometrics specialties to shrink in 2006 to 2010. A corresponding comparison of the knowledge base and research front of IS 2006-2010 suggests a continuing decline of the IR systems specialty in the near future, but also a considerable (re)growth of the webometrics area after a period of decline from 2001 to 2005 and 2006 to 2010, with the latter due perhaps in part to its contribution to an emerging web science.
  4. Huang, M.-H.; Wu, L.-L.; Wu, Y.-C.: ¬A study of research collaboration in the pre-web and post-web stages : a coauthorship analysis of the information systems discipline (2015) 0.00 
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  5. Shema, H.; Bar-Ilan, J.; Thelwall, M.: How is research blogged? : A content analysis approach (2015) 0.00 
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  6. Amolochitis, E.; Christou, I.T.; Tan, Z.-H.; Prasad, R.: ¬A heuristic hierarchical scheme for academic search and retrieval (2013) 0.00 
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  7. McKeown, K.; Daume III, H.; Chaturvedi, S.; Paparrizos, J.; Thadani, K.; Barrio, P.; Biran, O.; Bothe, S.; Collins, M.; Fleischmann, K.R.; Gravano, L.; Jha, R.; King, B.; McInerney, K.; Moon, T.; Neelakantan, A.; O'Seaghdha, D.; Radev, D.; Templeton, C.; Teufel, S.: Predicting the impact of scientific concepts using full-text features (2016) 0.00 
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  8. Bornmann, L.; Thor, A.; Marx, W.; Schier, H.: ¬The application of bibliometrics to research evaluation in the humanities and social sciences : an exploratory study using normalized Google Scholar data for the publications of a research institute (2016) 0.00 
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  9. Vinkler, P.: Core indicators and professional recognition of scientometricians (2017) 0.00 
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    Abstract
    The publication performance of 30 scientometricians is studied. The individuals are classified into 3 cohorts according to their manifested professional recognition, as Price medalists (Pm), members of the editorial board of Scientometrics and the Journal of Informetrics (Rw), and session chairs (Sc) at an International Society of Scientometrics and Informetrics (ISSI) conference. Several core impact indicators are calculated: h, g, p, citation distribution score (CDS), percentage rank position (PRP), and weight of influence of papers (WIP10). The indices significantly correlate with each other. The mean value of the indices of the cohorts decreases parallel with the decrease in professional recognition: Pm?>?Rw?>?Sc. The 30 scientometricians studied were clustered according to the core impact indices. The members in the clusters so obtained overlap only partly with the members in the cohorts made by professional recognition. The Total Overlap is calculated by dividing the sum of the diagonal elements in the cohorts-clusters matrix with the total number of elements, times 100. The highest overlap (76.6%) was obtained with the g-index. Accordingly, the g-index seems to have the greatest discriminative power in the system studied. The cohorts-clusters method may be used for validating scientometric indicators.
  10. Mutz, R.; Wolbring, T.; Daniel, H.-D.: ¬The effect of the "very important paper" (VIP) designation in Angewandte Chemie International Edition on citation impact : a propensity score matching analysis (2017) 0.00 
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  11. Fang, H.: ¬A discussion of citations from the perspective of the contribution of the cited paper to the citing paper (2018) 0.00 
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  12. Park, H.; You, S.; Wolfram, D.: Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields (2018) 0.00 
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  13. Chen, L.; Fang, H.: ¬An automatic method for ex-tracting innovative ideas based on the Scopus® database (2019) 0.00 
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  14. Aung, H.H.; Zheng, H.; Erdt, M.; Aw, A.S.; Sin, S.-C.J.; Theng, Y.-L.: Investigating familiarity and usage of traditional metrics and altmetrics (2019) 0.00 
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  15. Gingras, Y.: Bibliometrics and research evaluation : uses and abuses (2016) 0.00 
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    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.

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