Understanding Science with Large Language Models? : Potentials for the History, Philosophy, and Sociology of Science

Michael Zichert

ISBN 10: 3837679942 / ISBN 13: 9783837679946

Edité par Transcript Verlag Mai 2026, 2026

A propos de cet article

Neuware - How are large language models (LLMs) changing research in the history, philosophy, and sociology of science (HPSS) The contributors to this volume show how these tools open new possibilities for interpretative scholarship while posing fresh challenges for fields that thrive on qualitative methods, nuance, and historical depth. In essays, dialogues, and provocations, they capture a field in motion at a pivotal moment, driven by the rise of AI. These insights speak not only to HPSS scholars but also to readers across the humanities, social sciences, and AI-related fields, positioning HPSS as a bridge for understanding and shaping how LLMs enter research and society.

N° de réf. du vendeur 9783837679946

Détails bibliographiques

• Titre: Understanding Science with Large Language Models? : Potentials for the History, Philosophy, and Sociology of Science
• Éditeur: Transcript Verlag Mai 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 10 chiffres: 3837679942
• ISBN à 13 chiffres: 9783837679946
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 225 L x 148 H x 34 P (en millimètres) 225 millimètres de largeur par 148 millimètres de hauteur par 34 millimètres de profondeur

A propos de ce titre

Synopsis

How are large language models (LLMs) changing research in the history, philosophy, and sociology of science (HPSS)? The contributors to this volume show how these tools open new possibilities for interpretative scholarship while posing fresh challenges for fields that thrive on qualitative methods, nuance, and historical depth. In essays, dialogues, and provocations, they capture a field in motion at a pivotal moment, driven by the rise of AI. These insights speak not only to HPSS scholars but also to readers across the humanities, social sciences, and AI-related fields, positioning HPSS as a bridge for understanding and shaping how LLMs enter research and society.

À propos de l?auteur

Arno Simons is a postdoctoral researcher at Humboldt-Universität zu Berlin and a member of the project Network Epistemology in Practice (NEPI). With a background in the history, philosophy, and sociology of science, he studies how scientists interact with one another, policymakers, journalists, and the wider public. His research combines qualitative methods with computational approaches, including current work on large language models as tools for HPSS. He is also known for contributions to the study of policy instrument constituencies, environmental governance, and biomedical translation. Adrian Wüthrich is a guest professor for the History and Philosophy of Modern Science at Technische Universität Berlin. He leads the project Network Epistemology in Practice (NEPI), funded by a European Research Council Consolidator Grant, which investigates collective knowledge generation in large collaborations. His earlier work addressed interpretational debates in quantum mechanics, the role of images in science, causal reasoning in discovery processes, and the history of concepts in physics. His research often combines historical, philosophical, and computational approaches. Michael Zichert is a PhD candidate in the project Network Epistemology in Practice at Technische Universität Berlin, where he studies how scientific knowledge is produced in large-scale collaborations. He holds a BA in History of Science from Technische Universität Berlin and an MA in Digital Humanities from Universität Leipzig. His research combines computational methods with the history of science, focusing on diachronic semantic change, social network analysis, and the application of large language models in the field. Gerd Graßhoff (Prof. Dr.) is a professor for the History of Ancient Science at Humboldt-Universität zu Berlin and former director of the Excellence Cluster Topoi. He pioneered computational approaches to the history of science, applying machine learning and causal reasoning to ancient astronomical texts and scientific discovery processes. A Max Planck Fellow and member of Deutsche Akademie der Naturforscher Leopoldina, his research spans from Babylonian astronomy to digital humanities and AI methods for historical research.