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From productivity to wellbeing? Topic modelling of doctoral education research.

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Title: From productivity to wellbeing? Topic modelling of doctoral education research.
Authors: Smirnov, Nikita1,2 (AUTHOR) nsmirnov@hse.ru
Source: Higher Education (00181560). Mar2026, Vol. 91 Issue 3, p993-1021. 29p.
Subject Terms: *Educational productivity, *Doctoral degree, Well-being, Eurocentrism, University research, Trend analysis, Natural language processing, Thematic analysis
Abstract: Doctoral education has undergone significant transformations over the past two decades, driven by massification, internationalization, and the diversification of training models. These shifts have led to a growing body of research on doctoral education, yet little is known about the overarching thematic and geographical trends shaping this field. This study applies computational natural language processing techniques, including topic modeling and Named Entity Recognition, to analyze 5593 research articles from the SCOPUS database published between 1998 and 2022. Our findings reveal two key insights. First, we identify a thematic shift in doctoral education research, with increasing emphasis on topics related to doctoral students' well-being, identity, and experience, while earlier concerns about productivity and efficiency have declined. Second, we document a geographical imbalance in knowledge production, with the Global North dominating both authorship and empirical cases. The results contribute to ongoing discussions about the changing axiological landscape of doctoral education and the Eurocentric nature of global academic discourse. We discuss the methodological implications of our findings and highlight the need for further research into underrepresented perspectives. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Doctoral education has undergone significant transformations over the past two decades, driven by massification, internationalization, and the diversification of training models. These shifts have led to a growing body of research on doctoral education, yet little is known about the overarching thematic and geographical trends shaping this field. This study applies computational natural language processing techniques, including topic modeling and Named Entity Recognition, to analyze 5593 research articles from the SCOPUS database published between 1998 and 2022. Our findings reveal two key insights. First, we identify a thematic shift in doctoral education research, with increasing emphasis on topics related to doctoral students' well-being, identity, and experience, while earlier concerns about productivity and efficiency have declined. Second, we document a geographical imbalance in knowledge production, with the Global North dominating both authorship and empirical cases. The results contribute to ongoing discussions about the changing axiological landscape of doctoral education and the Eurocentric nature of global academic discourse. We discuss the methodological implications of our findings and highlight the need for further research into underrepresented perspectives. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2026
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