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Measuring the meaning of words in contexts: An automated analysis of controversies about 'Monarch butterflies,' 'Frankenfoods,' and 'stem cells'

Author

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  • Loet Leydesdorff

    (University of Amsterdam, Amsterdam School of Communications Research (ASCoR))

  • Iina Hellsten

    (Royal Netherlands Academy of Arts and Sciences, Virtual Knowledge Studio)

Abstract

Summary Co-words have been considered as carriers of meaning across different domains in studies of science, technology, and society. Words and co-words, however, obtain meaning in sentences, and sentences obtain meaning in their contexts of use. At the science/society interface, words can be expected to have different meanings: the codes of communication that provide meaning to words differ on the varying sides of the interface. Furthermore, meanings and interfaces may change over time. Given this structuring of meaning across interfaces and over time, we distinguish between metaphors and diaphors as reflexive mechanisms that facilitate the translation between contexts. Our empirical focus is on three recent scientific controversies: Monarch butterflies, Frankenfoods, and stem-cell therapies. This study explores new avenues that relate the study of co-word analysis in context with the sociological quest for the analysis and processing of meaning.

Suggested Citation

  • Loet Leydesdorff & Iina Hellsten, 2006. "Measuring the meaning of words in contexts: An automated analysis of controversies about 'Monarch butterflies,' 'Frankenfoods,' and 'stem cells'," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(2), pages 231-258, May.
    • Handle: RePEc:spr:scient:v:67:y:2006:i:2:d:10.1007_s11192-006-0096-y
    DOI: 10.1007/s11192-006-0096-y
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    Cited by:

    1. Ciarli, Tommaso & Ràfols, Ismael, 2019. "The relation between research priorities and societal demands: The case of rice," Research Policy, Elsevier, vol. 48(4), pages 949-967.
    2. Peter W. Liesch & Lars Håkanson & Sara L. McGaughey & Stuart Middleton & Julia Cretchley, 2011. "The evolution of the international business field: a scientometric investigation of articles published in its premier journal," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 17-42, July.
    3. Matias Federico Milia & Ariadna Nebot Giralt & Rigas Arvanitis, 2022. "Local emergence, global expansion: understanding the structural evolution of a bi-lingual national research landscape," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7369-7395, December.
    4. Sasson, Elan & Ravid, Gilad & Pliskin, Nava, 2015. "Improving similarity measures of relatedness proximity: Toward augmented concept maps," Journal of Informetrics, Elsevier, vol. 9(3), pages 618-628.
    5. Oscar Stuhler, 2022. "Who Does What to Whom? Making Text Parsers Work for Sociological Inquiry," Sociological Methods & Research, , vol. 51(4), pages 1580-1633, November.
    6. Fengjun Sun & Lijun Zhu, 2012. "Citation genetic genealogy: a novel insight for citation analysis in scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 577-589, May.
    7. Bart Thijs, 2020. "Using neural-network based paragraph embeddings for the calculation of within and between document similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 835-849, November.
    8. Yi Bu & Mengyang Li & Weiye Gu & Win‐bin Huang, 2021. "Topic diversity: A discipline scheme‐free diversity measurement for journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 523-539, May.
    9. Jian Zhang & Michael S. Vogeley & Chaomei Chen, 2011. "Scientometrics of big science: a case study of research in the Sloan Digital Sky Survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 1-14, January.

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