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A structural topic model approach to scientific reorientation of economics and chemistry after German reunification

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  • Andreas Rehs

    (University of Kassel)

Abstract

The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter.

Suggested Citation

  • Andreas Rehs, 2020. "A structural topic model approach to scientific reorientation of economics and chemistry after German reunification," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1229-1251, November.
  • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03640-0
    DOI: 10.1007/s11192-020-03640-0
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    Cited by:

    1. Löw, Franziska, 2022. "Biased reporting by the German media?," Working Paper 193/2022, Helmut Schmidt University, Hamburg.
    2. Lopreite, Milena & Misuraca, Michelangelo & Puliga, Michelangelo, 2023. "An analysis of the thematic evolution of ageing and healthcare expenditure using word embedding: A scoping review of policy implications," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    3. Buehling, Kilian, 2021. "Changing research topic trends as an effect of publication rankings – The case of German economists and the Handelsblatt Ranking," Journal of Informetrics, Elsevier, vol. 15(3).
    4. Rehs, Andreas, 2021. "A supervised machine learning approach to author disambiguation in the Web of Science," Journal of Informetrics, Elsevier, vol. 15(3).

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    More about this item

    Keywords

    Topic modelling; German reunification; Dissertations; Structural topic modelling; Research field mapping;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • P30 - Political Economy and Comparative Economic Systems - - Socialist Institutions and Their Transitions - - - General
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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