Computational conceptual history of scientific concepts: From early digital methods to LLMs

2026-06-02

AI Summary

This study systematically examines the evolution, applicability, and limitations of computational approaches—particularly large language models (LLMs)—in the historical study of scientific concepts. Situating LLMs within a broader methodological lineage that spans early digital humanities, distributional semantic models, and semantic change detection, the work offers a comparative analysis of how different techniques handle corpus construction, conceptual operationalization, and interpretive logic. The research clarifies both the continuities and novel contributions of LLMs in this domain and, for the first time, establishes a methodological evaluation framework for their use in history and philosophy of science and technology (HPSS). This framework aims to inform and critically guide future methodological choices in the field.

Abstract

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.

Problem

• computational conceptual history
• large language models
• lexical semantic change
• history of science
• methodological challenges

Innovation

• computational conceptual history
• large language models
• lexical semantic change
• digital humanities
• methodological integration