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Journal articles on the topic 'Informetrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Zhao, Rongying, Fengjiao Guo, and Zhuheng Fu. "Investigation and analysis of Informetrics curriculum education in China." Library Hi Tech 34, no. 3 (September 19, 2016): 539–48. http://dx.doi.org/10.1108/lht-08-2015-0080.

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Purpose The purpose of this paper is to generalize the teaching characteristics of some typical colleges in China which offer Informetrics courses, and to propose useful suggestions for the course construction of Informetrics through empirical investigation. Design/methodology/approach The statuses of Informetrics curriculum education of the top ten universities in Library and Information Science and Archives Management in Chinese mainland as well as Fu Jen Catholic University in Taiwan have been investigated and analyzed through a network survey and a telephone survey. Wuhan University and Fu Jen Catholic University are considered as the typical examples. Findings The educational development of Informetrics in China has gone through three stages: the initial stage, the developing stage and the improving stage. Based on the empirical investigation and analysis, some meaningful conclusions are drawn concerning the education system, teaching objectives, teaching contents and teaching methods of Informetrics courses. Originality/value This paper provides curricular information about Informetrics courses in China.

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2

Lazarev, Vladimir. "Bibliometrics, Scientometrics and Informetrics. Part 3. Object (Ending)." Science Management: Theory and Practice 3, no. 2 (June 30, 2021): 99–136. http://dx.doi.org/10.19181/smtp.2021.3.2.5.

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A simplified and sometimes vulgar understanding of the role of scientometrics in science management makes it necessary to better understand its essential characteristics. In this paper, scientometrics is considered in interrelations with bibliometrics and informetrics that are the fields of knowledge closest to it.In relation to bibliometrics, scientometrics and informetrics, this part discusses the representationof their object. Its reinterpretation using the modern broad meaning of the term “document” made us possible to come to the conclusion about (at least) the maximum convergence of interpretations of objects of bibliometrics, scientometrics and informetrics. In any case, such a comparative analysis of objects helps to identify both similarities and differences between these fields of knowledge, which is important, since their awareness is a very obvious initial condition for mutual conceptual enrichment (artificially diverged?) bibliometrics, scientometrics and informetrics with knowledge and concepts.The present, i. e. the third part of the paper provides examples of understanding the objects of bibliometrics, scientometrics, and informetrics using the modern, “broad” meaning of the concept of “document”. It is shown that with the use of this approach, the interpretations of the objects of bibliometrics, scientometrics and informetrics are even closer than with the use of the traditional one. Also, the cognitive capabilities of bibliometrics, scientometrics and informetrics are potentially expanded in this case. The ways of possible further research in this direction are outlined, taking into account the V. S. Stepin’s viewpoints on the subject of research as a concept dependent on the applied methods, and on the empirical object asthe essence allocated in connection with the tasks of cognition.

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3

Burrell, Quentin L. "Extending Lotkaian informetrics." Information Processing & Management 44, no. 5 (September 2008): 1794–807. http://dx.doi.org/10.1016/j.ipm.2008.03.002.

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4

Lazarev, Vladimir. "Bibliometrics, scientometrics and informetrics. Part 2. Object." Science Management: Theory And Practice 3, no. 1 (March 25, 2021): 80–105. http://dx.doi.org/10.19181/smtp.2021.3.1.5.

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A simplified and sometimes vulgar understanding of the role of scientometrics in science management makes it necessary to better understand its essential characteristics. In this paper, scientometrics is considered in interrelations with bibliometrics and informetrics that are the fields of knowledge closest to it.In relation to bibliometrics, scientometrics and informetrics, this part discusses the representationof their object. Its reinterpretation using the modern broad meaning of the term “document” made us possible to come to the conclusion that there is (at least) the maximum convergence of interpretations of objects of bibliometrics, scientometrics and informetrics. In any case, such a comparative analysis of objects helps to identify both similarities and differences between these fields of knowledge, which is important, since their awareness is a very obvious initial condition for mutual conceptual enrichment (artificially diverged?) bibliometrics, scientometrics and informetrics with knowledge and concepts.Part 2 of the present paper provides examples of treating the objects of bibliometrics, scientometricsand informetrics using traditional interpretations of the concept of a “document”, followed by brief a review of the reinterpretation of this concept. However, consideration of the relationship of a “document” and “information” is much more detailed in the present work than it was performed in the author’s previous paper on this topic.

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5

Sedighi, Mehri. "Application of word co-occurrence analysis method in mapping of the scientific fields (case study: the field of Informetrics)." Library Review 65, no. 1/2 (February 1, 2016): 52–64. http://dx.doi.org/10.1108/lr-07-2015-0075.

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Purpose – The purpose of this article is to investigate the use of word co-occurrence analysis method in mapping of the scientific fields with emphasis on the field of Informetrics. Design/methodology/approach – This is an applied study using scientometrics, co-word analysis and network analysis and its steps are summarised as follows: collecting the data related to the Informetrics field indexed in Web of Science (WOS) database, refining and standardising the keywords of the extracted articles from WOS and preparing a selected list of these keywords, drawing the word co-occurrence map in the Informetrics field and analysing of results. Findings – Based on the resulted maps the concepts such as information science, library, bibliometric analysis, innovation and text mining are the most widely used topics in the field of Informetrics. The co-word occurrence maps drawn at different periods show the changes and stabilities in the concepts related to the field of Informetrics. A number of topics such as “bibliometric analysis” are present in all years, whereas others such as “innovation” have disappeared. New topics emerge as a recombination of existing topics and in interaction with new (technological) developments. Originality/value – The results of these analytical studies can be used as a guide for determining research priorities in the scientific fields, and also for planning and management in academic institutions.

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6

Lazarev, Vladimir. "Bibliometrics, scientometrics and informetrics. Part 1. Emergence and background." Science Management: Theory and Practice 2, no. 4 (December 23, 2020): 133–63. http://dx.doi.org/10.19181/smtp.2020.2.4.6.

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A simplified and sometimes vulgar understanding of the role of scientometrics in science management makes it necessary to better understand its essential characteristics. In this paper, scientometrics is considered in interrelations with bibliometrics and informetrics that are the fields of knowledge closest to it. Briefly considered are the published works that mark their emergence and the ones that represent their background; the objects of bibliometrics, scientometrics and informetrics are reinterpreted and the methods are surveyed. The subject of the first part of the present paper is the emergence and background of scientometrics and the related fields of knowledge. The main conclusion is that the methodological proximity of bibliometrics, scientometrics and informetrics borders on their indistinguishability.

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7

Kokowski, Michał. "Scientometric, bibliometric and informetric bibliography (Selection)." Studia Historiae Scientiarum 14 (May 27, 2015): 185–266. http://dx.doi.org/10.4467/23921749pkhn_pau.16.009.5265.

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The text presents a selection of bibliography on scientometrics, bibliometrics and informetrics. The bibliography was chosen in the context of the author’s research of: a) the current debate on scientometrics, bibliometrics and informetrics in Poland, b) the history of these disciplines, and c) the history of the science of science. This selection has an important advantage because it includes many publications that a) represent the views both of Polish and foreign authors, b) discuss serious methodological limitations of scientometrics, bibliometrics and informetrics and c) show the inseparable connection between the disciplines and the science of science. This bibliography was already used in two of the author’s articles published in Prace Komisji Historii Nauki PAU, volume 14 (2015).

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8

Tague-Sutcliffe, Jean. "An introduction to informetrics." Information Processing & Management 28, no. 1 (January 1992): 1–3. http://dx.doi.org/10.1016/0306-4573(92)90087-g.

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9

Egghe, Leo. "Type/Token-Taken informetrics." Journal of the American Society for Information Science and Technology 54, no. 7 (2003): 603–10. http://dx.doi.org/10.1002/asi.10247.

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10

Liu, Xiaojuan, Yu Wei, and Zhuojing Zhao. "How researchers view altmetrics: an investigation of ISSI participants." Aslib Journal of Information Management 72, no. 3 (April 23, 2020): 361–78. http://dx.doi.org/10.1108/ajim-07-2019-0165.

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PurposeThe purpose of this study is to explore informetrics researchers' use of social media for academic activities, their attitudes to the applicability of altmetrics in research evaluation, the factors influencing their attitudes, and the main opportunities and weaknesses of using altmetrics.Design/methodology/approachA survey using a questionnaire was conducted with researchers who participated in the 16th International Conference on Scientometrics and Informetrics ISSI 2017 and a sample of 125 respondents was obtained.FindingsProgressively more researchers are using social media for different types of academic activities. The study found that many factors affect informetrics researchers' attitudes in different application scenarios with respect to research evaluation. Researchers who have studied altmetrics and who began using social media platforms recently or frequently have more positive attitudes. Academic users and social users have statistically significantly disparate attitudes toward altmetrics in different disciplines and different application scenarios.Research limitations/implicationsOur study only focused on 125 informetrics researchers, who participated in ISSI 2017. We mainly used the questionnaire method, but did not conduct in-depth interviews with the researcher's views.Originality/valueInformetrics researchers are participants in social media and major researchers of altmetrics. Previous research has examined their use of social media, and this study combines this use of social media with their attitudes to altmetrics to explore the value of altmetrics from a particular perspective. The paper also provides suggestions for the application of altmetrics in research evaluation.

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11

Egghe, L. "Five years “Journal of Informetrics”." Journal of Informetrics 6, no. 3 (July 2012): 422–26. http://dx.doi.org/10.1016/j.joi.2012.02.003.

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12

Bornmann, Lutz, and Hans-Dieter Daniel. "Count regression models in informetrics." Journal of Informetrics 10, no. 1 (February 2016): 29–30. http://dx.doi.org/10.1016/j.joi.2015.10.003.

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13

Rousseau, R. "Similarities between informetrics and econometrics." Scientometrics 30, no. 2-3 (June 1994): 385–87. http://dx.doi.org/10.1007/bf02018108.

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14

Egghe, Leo. "Positive reinforcement and 3-dimensional informetrics." Scientometrics 60, no. 3 (2004): 497–509. http://dx.doi.org/10.1023/b:scie.0000034390.96418.bf.

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15

Das, Prabir Kumar. "Journal of Informetrics: A Bibliometric Profile." DESIDOC Journal of Library & Information Technology 33, no. 3 (May 1, 2013): 243–52. http://dx.doi.org/10.14429/djlit.33.3.4610.

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16

Spaan, Jos A. E. "The danger of pseudoscience in Informetrics." Journal of Informetrics 4, no. 3 (July 2010): 439–40. http://dx.doi.org/10.1016/j.joi.2010.03.010.

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17

Egghe, Leo. "Welcome to the Journal of Informetrics." Journal of Informetrics 1, no. 1 (January 2007): 1. http://dx.doi.org/10.1016/j.joi.2006.09.003.

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18

Russell, Jane M. "Back to the future for informetrics." Scientometrics 30, no. 2-3 (June 1994): 407–10. http://dx.doi.org/10.1007/bf02018113.

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19

Wolfram, Dietmar. "Applications Of Informetrics To Information Retrieval Research." Informing Science: The International Journal of an Emerging Transdiscipline 3 (2000): 077–82. http://dx.doi.org/10.28945/581.

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20

Egghe, Leo. "Lotkaian informetrics and applications to social networks." Bulletin of the Belgian Mathematical Society - Simon Stevin 16, no. 4 (November 2009): 689–703. http://dx.doi.org/10.36045/bbms/1257776242.

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21

Lazarev, Vladimir. "Bibliometrics, scientometrics and informetrics. Part 4. Object." Science Management: Theory and Practice 4, no. 1 (March 28, 2022): 180–214. http://dx.doi.org/10.19181/smtp.2022.4.1.10.

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A simplified and sometimes vulgar understanding of the role of scientometrics in the management of science makes it necessary to better understand its essential characteristics. In this paper, scientometrics is considered in interrelations with bibliometrics and informetrics that are the fields of knowledge closest to it. In relation to the three mentioned “metrics”, this part discusses the representation of their methods. Consideration of the methodological “arsenal” of the three “metrics” made us possible to come to the conclusion that there are no such specific methods in any of them, the presence of which would be a distinctive feature of exactly and exclusively this “metrics”. The opinion is expressed that the high prevalenceof some methodological component in any of the “metrics”, the use of general scientific methods in them and the use of econometrics and sociometrics techniques in scientometric research cannot be treated as distinguishing features of this or that “metrics”, as the principle of delimitation of one “metrics” from another. As it follows from the very term “metrics”, the methods of bibliometrics, scientometrics and informetrics are methods of quantitative research. The emergence of new “metrics” methods was characteristic of the entire twentieth century, and, of course, there is no reason to believe that the list of these methods known today is final, complete.

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22

Wormell, Irene. "Informetrics -- a new area of quantitative studies." Education for Information 18, no. 2-3 (July 1, 2000): 131–38. http://dx.doi.org/10.3233/efi-2000-182-304.

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23

Tang, Rong. "Applied Informetrics for Information Retrieval Research (review)." portal: Libraries and the Academy 4, no. 3 (2004): 431–32. http://dx.doi.org/10.1353/pla.2004.0056.

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24

Wormell, Irene. "Informetrics: an emerging subdiscipline in information science." Asian Libraries 7, no. 10 (October 1998): 257–68. http://dx.doi.org/10.1108/10176749810241838.

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25

Rousseau, R. "Category theory and informetrics: Information production processes." Scientometrics 25, no. 1 (September 1992): 77–87. http://dx.doi.org/10.1007/bf02016848.

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26

Egghe, L. "Generalized transfer principles in econometrics and informetrics." Scientometrics 25, no. 1 (September 1992): 167–91. http://dx.doi.org/10.1007/bf02016853.

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27

Egghe, L. "Bridging the gaps: Conceptual discussions on informetrics." Scientometrics 30, no. 1 (May 1994): 35–47. http://dx.doi.org/10.1007/bf02017211.

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28

Turner, W. A. "What's in an R: Informetrics or infometrics?" Scientometrics 30, no. 2-3 (June 1994): 471–80. http://dx.doi.org/10.1007/bf02018127.

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29

Galyavieva, M. S., and A. M. Elizarov. "Informetrics in the World System of Higher Education." Scientific and Technical Information Processing 44, no. 4 (October 2017): 297–301. http://dx.doi.org/10.3103/s014768821704013x.

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30

Guns, Raf. "The three dimensions of informetrics: a conceptual view." Journal of Documentation 69, no. 2 (March 2013): 295–308. http://dx.doi.org/10.1108/00220411311300084.

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31

McKiernan, Gerry. "Bibliometrics, Cybermetrics, Informetrics, and Scientometrics Sites and Sources." Science & Technology Libraries 26, no. 2 (December 9, 2005): 107–15. http://dx.doi.org/10.1300/j122v26n02_06.

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32

Daraio, Cinzia, Henk F. Moed, Giuseppe Catalano, Giancarlo Ruocco, Cassidy R. Sugimoto, and Wolfgang Glänzel. "The 17th International Conference on Scientometrics and Informetrics." Scientometrics 125, no. 2 (November 2020): 831–34. http://dx.doi.org/10.1007/s11192-020-03768-z.

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33

Das, Anup Kumar. "Understanding Informetrics and Scholarly Communications in a Nutshell." Journal of Scientometric Research 5, no. 2 (August 1, 2016): 170–72. http://dx.doi.org/10.5530/jscires.5.2.9.

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34

Burrell, Quentin L. "Time-dependent aspects of co-concentration in informetrics." Scientometrics 73, no. 2 (August 3, 2007): 161–74. http://dx.doi.org/10.1007/s11192-007-1688-x.

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35

Leta, Jacqueline, Birger Larsen, Ronald Rousseau, and Wolfgang Glänzel. "The 12th International conference on scientometrics and informetrics." Scientometrics 83, no. 3 (April 22, 2010): 603–4. http://dx.doi.org/10.1007/s11192-010-0219-3.

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36

Ocholla, Dennis, Peter Ingwersen, Ed C. M. Noyons, and Wolfgang Glänzel. "The 13th International Conference on Scientometrics and Informetrics." Scientometrics 91, no. 2 (January 7, 2012): 315–16. http://dx.doi.org/10.1007/s11192-011-0608-2.

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37

Wolfram, Dietmar. "The symbiotic relationship between information retrieval and informetrics." Scientometrics 102, no. 3 (November 25, 2014): 2201–14. http://dx.doi.org/10.1007/s11192-014-1479-0.

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38

Niemi, Timo, Lasse Hirvonen, and Kalervo Järvelin. "Multidimensional Data Model and Query Language for Informetrics." Journal of the American Society for Information Science and Technology 54, no. 10 (May 20, 2003): 939–51. http://dx.doi.org/10.1002/asi.10290.

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39

Peters, Isabella, and Judit Bar-Ilan. "Informetrics, bibliometrics, altmetrics: What is it all about?" Proceedings of the American Society for Information Science and Technology 51, no. 1 (2014): 1–4. http://dx.doi.org/10.1002/meet.2014.14505101015.

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40

Yu, Fan, Junping Qiu, and Wen Lou. "Library resources semantization based on resource ontology." Electronic Library 32, no. 3 (May 27, 2014): 341–62. http://dx.doi.org/10.1108/el-05-2012-0056.

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Purpose – This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of library resources by resource ontology (RO). Design/methodology/approach – The paper constructed an RO model. Methods of informetrics are utilized to reveal semantic relationships among library resources. Methods of ontology, ontology-relational database mapping (O-R mapping) and relational database modelling are utilized to construct RO. Take author co-occurrence for example, the paper demonstrated the capability of RO model. Findings – RO not only revealed the deep-level semantic relationships of metadata of library resources but also realized totally computer-automated processing. RO improved the efficiency of knowledge organization and discovery. Research limitations/implications – Semantic relationships revealed by RO are limited to simple metadata, which makes it difficult to reveal fine-grained semantic relationships. Ongoing research focuses on the revelation of semantic relationships based on the title and abstract. Practical implications – The paper includes implications for utilizing methods of Informetrics to construct ontology. Originality/value – This paper proposed a standardized process of ontology construction in library resources. It may be of potential interest for anyone who needs to effectively organize library resources.

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41

Loan, Fayaz Ahmad, and Ufaira Yaseen Shah. "The decay and persistence of web references." Digital Library Perspectives 36, no. 2 (May 9, 2020): 157–66. http://dx.doi.org/10.1108/dlp-02-2020-0013.

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Purpose The purpose of this study is to identify the persistence and decay of uniform resource locator (URLs) associated with Web references. The decaying of Web references is analyzed in relation to their age, domain, technical errors and error codes. Design/methodology/approach The Web references of the Journal of Informetrics were selected for analysis and interpretation to fulfill the set objectives. The references of all the scholarly articles, excluding editorials and reviews published in the Journal of Informetrics for five years from 2007 to 2011 were recorded in a text file. Later, the URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The collected data were then transferred into an excel file and tabulated for further analysis and interpretation using simple statistical techniques. Findings The results showed that of the total 7,409 citations retrieved from 221 articles, 358 citations (4.8%) were Web citations. These Web citations were assessed to find their persistence and decay. The results reveal that 115 (32.12%) Web references were missing or dead. The most common error associated with the missing Web citations was Error 404 Page not found, contributing 60% of the total missing citations, followed by 400 Bad Request Error (35.65%). The domain analysis of missing Web citations depicts that most of the missing URLs were associated with the .gov domain (40%), followed by .edu (29.58%) and .com (26.04%). Research limitations/implications The Web references of a single journal, namely, Journal of Informetrics, were analyzed for five years, and hence, the generalization of findings needs to be cautioned. Practical implications The URL decay is becoming a major problem in the preservation and citation of the Web resources, and collaborative efforts are needed to reduce the decaying of URLs. Originality/value A good number of studies have been conducted to analyze the persistence and decay of Web references, as it is the hot topic of research across disciplines, and this study is a step further in the same direction.

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42

Galyavieva, M. S. "On the formation of the concept of informetrics (Review)." Scientific and Technical Information Processing 40, no. 2 (April 2013): 89–96. http://dx.doi.org/10.3103/s014768821302007x.

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43

Nam, Eunkyung, and Ji-Hong Park. "Factors Influencing Research Collaboration in the Field of Informetrics." Journal of the Korean Society for information Management 31, no. 4 (December 30, 2014): 201–27. http://dx.doi.org/10.3743/kosim.2014.31.4.201.

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44

Hassanzadeh, Mohammad, and Emran Ghorbani. "User Interaction with Online Information Resources: An Informetrics Approach." International Journal of Computational Linguistics Research 9, no. 4 (December 1, 2018): 156. http://dx.doi.org/10.6025/jcl/2018/9/4/156-164.

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45

Egghe, L. "Expansion of the field of informetrics: Origins and consequences." Information Processing & Management 41, no. 6 (December 2005): 1311–16. http://dx.doi.org/10.1016/j.ipm.2005.03.011.

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46

Liu, Yuxian, and Ronald Rousseau. "A continuous description of discrete data points in informetrics." Aslib Proceedings 64, no. 2 (March 16, 2012): 193–200. http://dx.doi.org/10.1108/00012531211215204.

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47

Glänzel, Wolfgang, and Henk F. Moed. "Preface: The 11th International Conference on Scientometrics and Informetrics." Scientometrics 79, no. 1 (March 24, 2009): 5. http://dx.doi.org/10.1007/s11192-009-0400-8.

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48

Burrell, Quentin L. "?Type/Token-Taken? informetrics: Some comments and further examples." Journal of the American Society for Information Science and Technology 54, no. 13 (2003): 1260–63. http://dx.doi.org/10.1002/asi.10324.

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49

Acker, A. "Basic data." Symposium - International Astronomical Union 180 (1997): 10–17. http://dx.doi.org/10.1017/s0074180900129663.

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Basic data on planetary nebulae concern all the fields related to these objects. As an illustration of the increasing importance of planetary nebulae in large fields of contemporary science, let me note that, in a communication for a conference on “bibliometrics, informetrics and scientometrics”, Bassecoulard and Zitt (1994) chose planetary nebulae as the best example of a complicated network allowing and needing interesting cluster analysis.

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50

Chen, Mu-Yen, Chien-Hsiang Liao, Edwin David Lughofer, and Erol Egrioglu. "Informetrics on social network mining: research, policy and practice challenges." Library Hi Tech 38, no. 2 (June 8, 2020): 273–75. http://dx.doi.org/10.1108/lht-06-2020-279.

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