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Using global mapping to create more accurate document‐level maps of research fields

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  • Richard Klavans
  • Kevin W. Boyack

Abstract

We describe two general approaches to creating document‐level maps of science. To create a local map, one defines and directly maps a sample of data, such as all literature published in a set of information science journals. To create a global map of a research field, one maps “all of science” and then locates a literature sample within that full context. We provide a deductive argument that global mapping should create more accurate partitions of a research field than does local mapping, followed by practical reasons why this may not be so. The field of information science is then mapped at the document level using both local and global methods to provide a case illustration of the differences between the methods. Textual coherence is used to assess the accuracies of both maps. We find that document clusters in the global map have significantly higher coherence than do those in the local map, and that the global map provides unique insights into the field of information science that cannot be discerned from the local map. Specifically, we show that information science and computer science have a large interface and that computer science is the more progressive discipline at that interface. We also show that research communities in temporally linked threads have a much higher coherence than do isolated communities, and that this feature can be used to predict which threads will persist into a subsequent year. Methods that could increase the accuracy of both local and global maps in the future also are discussed.

Suggested Citation

  • Richard Klavans & Kevin W. Boyack, 2011. "Using global mapping to create more accurate document‐level maps of research fields," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 1-18, January.
  • Handle: RePEc:bla:jamist:v:62:y:2011:i:1:p:1-18
    DOI: 10.1002/asi.21444
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    1. Keungoui Kim & Dieter F. Kogler & Sira Maliphol, 2024. "Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    2. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    3. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    4. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    5. Yang, Siluo & Wang, Feifei, 2015. "Visualizing information science: Author direct citation analysis in China and around the world," Journal of Informetrics, Elsevier, vol. 9(1), pages 208-225.
    6. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    7. Shuo Xu & Junwan Liu & Dongsheng Zhai & Xin An & Zheng Wang & Hongshen Pang, 2018. "Overlapping thematic structures extraction with mixed-membership stochastic blockmodel," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 61-84, October.
    8. Edwin Horlings & Thomas Gurney, 2013. "Search strategies along the academic lifecycle," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1137-1160, March.
    9. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
    10. Chaomei Chen & Min Song, 2019. "Visualizing a field of research: A methodology of systematic scientometric reviews," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    11. Yan, Erjia & Ding, Ying & Milojević, Staša & Sugimoto, Cassidy R., 2012. "Topics in dynamic research communities: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 6(1), pages 140-153.
    12. Katalin Orosz & Illés J. Farkas & Péter Pollner, 2016. "Quantifying the changing role of past publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 829-853, August.
    13. Yu-Wei Chang, 2018. "Examining interdisciplinarity of library and information science (LIS) based on LIS articles contributed by non-LIS authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1589-1613, September.
    14. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    15. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    16. Loet Leydesdorff, 2013. "Statistics for the dynamic analysis of scientometric data: the evolution of the sciences in terms of trajectories and regimes," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 731-741, September.
    17. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    18. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    19. Carlos G. Figuerola & Francisco Javier García Marco & María Pinto, 2017. "Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1507-1535, September.
    20. Ryo Takahashi & Kenji Kaibe & Kazuyuki Suzuki & Sayaka Takahashi & Kotaro Takeda & Marc Hansen & Michiaki Yumoto, 2023. "New concept of the affinity between research fields using academic journal data in Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3507-3534, June.
    21. Yan, Erjia & Ding, Ying & Cronin, Blaise & Leydesdorff, Loet, 2013. "A bird's-eye view of scientific trading: Dependency relations among fields of science," Journal of Informetrics, Elsevier, vol. 7(2), pages 249-264.
    22. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    23. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    24. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    25. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.

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