Three reseachers from the University of Québec, Montréal – Christophe Malaterre, Jean-François Chartier, and Davide Pulizzotto (the latter also being an affiliate member of DR2 group) have recently published an intriguing paper entitled What Is This Thing Called Philosophy of Science? A Computational Topic-Modeling Perspective, 1934–2015. (See the abstract below).
In that paper, currently available as an online-first on HOPOS: The Journal of the International Society of the History of Philosophy of Science, they apply unsupervised text-mining methods to the complete corpus of the journal Philosophy of Science. They thus mapped the evolution of 126 topics over the 82 years of activity, and provided (seminal) interpretation of some of the many interesting patterns resulting from their analysis.
Some of the findings were also summarised by the portal Daily Nous on August 28th, 2019.
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Abstract: What is philosophy of science? Numerous manuals, anthologies, and essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this article, we address the question from a complementary perspective: we target the content of one major journal in the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key research topics that span 82 years. We also tracked those topics’ evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes in the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge, and the rise of a recent philosophy of biology and other
trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches.
