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The research topic landscape in the literature of social class and inequality

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  • Liang Guo
  • Shikun Li
  • Ruodan Lu
  • Lei Yin
  • Ariane Gorson-Deruel
  • Lawrence King

Abstract

The literature of social class and inequality is not only diverse and rich in sight, but also complex and fragmented in structure. This article seeks to map the topic landscape of the field and identify salient development trajectories over time. We apply the Latent Dirichlet Allocation topic modeling technique to extract 25 distinct topics from 14,038 SSCI articles published between 1956 to 2017. We classified three topics as “hot”, eight as “stable” and 14 as “cold”, based on each topic’s idiosyncratic temporal trajectory. We also listed the three most cited references and the three most popular journal outlets per topic. Our research suggests that future effort may be devoted to Topics “urban inequalities, corporate social responsibility and public policy in connected capitalism”, “education and social inequality”, “community health intervention and social inequality in multicultural contexts” and “income inequality, labor market reform and industrial relations”.

Suggested Citation

  • Liang Guo & Shikun Li & Ruodan Lu & Lei Yin & Ariane Gorson-Deruel & Lawrence King, 2018. "The research topic landscape in the literature of social class and inequality," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    • Handle: RePEc:plo:pone00:0199510
    DOI: 10.1371/journal.pone.0199510
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    3. Ray R. Larson, 2010. "Introduction to Information Retrieval," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(4), pages 852-853, April.
    4. Lucas, Christopher & Nielsen, Richard A. & Roberts, Margaret E. & Stewart, Brandon M. & Storer, Alex & Tingley, Dustin, 2015. "Computer-Assisted Text Analysis for Comparative Politics," Political Analysis, Cambridge University Press, vol. 23(2), pages 254-277, April.
    5. Ray R. Larson, 2010. "Introduction to Information Retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(4), pages 852-853, April.
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    Cited by:

    1. Bingke Zhu & Hao Fan & Bingbing Xie & Ran Su & Chaofeng Zhou & Jianping He, 2020. "Mapping the Scientific Research on Healthcare Workers’ Occupational Health: A Bibliometric and Social Network Analysis," IJERPH, MDPI, vol. 17(8), pages 1-22, April.

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