Computational conceptual history of scientific concepts: From early digital methods to LLMs
For historians, philosophers, and sociologists of science, this paper offers a methodological review situating LLMs within prior computational approaches, but it is primarily a survey without novel empirical results.
This paper reviews the history of computational approaches to concept analysis in HPSS, from early digital methods to LLMs, and examines what LLMs add, their inherited problems, and recent case studies. It provides an overview of challenges and opportunities in corpus construction, operationalization, and evaluation.
This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). We examine what LLMs add to existing methods, how they inherit longstanding problems, and review recent case studies that employ them. In the first part, we reconstruct computational conceptual history before LLMs by bringing together three strands of work: early digital methods in HPSS, distributional approaches from digital history and related research, and lexical semantic change detection. We provide an overview of the main challenges and opportunities, focusing on corpus construction, operationalization and modelling choices, and evaluation and interpretation. In the second part, we turn to the era of LLMs, starting with a short introduction to LLMs before reviewing LLM-based work on lexical semantic change detection and relevant case studies in HPSS. We then revisit the earlier methodological questions, showing how issues of corpus construction, model choice and training data, operationalization trade-offs, and evaluation and interpretation play out in LLM-based workflows.