2025/09/04 by Vilius Dranseika, Piotr Bystranowski, Tomasz Żuradzki · 1 voice
Computer Science · Social Sciences · #Computational and Text Analysis Methods #Topic Modeling
paper · doi:10.1136/jme-2025-111310
openalex publication_date 2025/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Abstract We respond to Gillon’s critique of our data-driven analysis of the history of Journal of Medical Ethics ( JME ), in which we used a topic model to trace intellectual trends in the journal’s first 50 years. Gillon, drawing on his personal memories as JME ’s second (and longest serving) editor, challenges several of our findings, particularly those concerning the prominence and classification of topics such as Ethics education . In this reply, we clarify misunderstandings that led to part of his criticisms of our method. At the same time, we also briefly discuss some nuances of topic modelling, in particular, its reliance on simplified representations of text, sensitivity to modeling choices and topic interpretations. Rather than viewing computational models and editorial memory as competing sources of insight, we propose that they are complementary: each illuminates different dimensions of the journal’s evolution. Gillon’s engagement with our work ultimately highlights the importance of methodological transparency and the value of combining digital humanities tools with lived experience in the historiography of academic disciplines.