Cover of book: Understanding Science with Large Language Models?
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Understanding Science with Large Language Models?

Potentials for the History, Philosophy, and Sociology of Science
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Publisher:
 2026

Summary

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.

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Bibliographic data

Edition
1/2026
Copyright Year
2026
ISBN-Print
978-3-8376-7994-6
ISBN-Online
978-3-8394-4752-9
Publisher
transcript, Bielefeld
Series
Science Studies
Volume
0
Language
English
Pages
523
Product Type
Edited Book

Table of contents

ChapterPages
  1. FrontmatterPages 1 - 8
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  2. ForewordPages 9 - 11
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  3. Doing science studies with large language modelsPages 13 - 32
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  4. Critical concerns for using LLMs in the (computational) humanities and beyondPages 35 - 50
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  5. AI and the scientistPages 51 - 55
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  6. Grounding AI in humanistic inquiryPages 57 - 69
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  7. On the use and limitations of large language models in historical scholarshipPages 71 - 99
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  8. Encoded humanities, or: not everything has to be generativePages 101 - 114
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  9. LLMs as philosophersPages 115 - 123
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  10. Why pursue temporally‐grounded AI for historical disciplines, and what makes it so challenging?Pages 127 - 141
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  11. Generative LLMs and history researchPages 143 - 150
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  12. Zero‐shot generation of synthetic historical data with LLMsPages 151 - 177
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  13. LLMs and multilingual historical corpora in a digital history projectPages 179 - 187
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  14. Epistemic framings in sciencePages 191 - 196
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  15. Computational conceptual history of scientific conceptsPages 197 - 215
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  16. Meaning at the Planck scale?Pages 217 - 241
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  17. Exploring disciplinary differences in semantic uniformityPages 243 - 256
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  18. Use of large language models in the classification of scientific texts into disciplinesPages 257 - 270
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  19. Discursive parallels of the chemical revolutionPages 271 - 290
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  20. The potential of LLMs for constructing a socio‐legal knowledge graphPages 293 - 305
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  21. From source to structurePages 307 - 334
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  22. Large language models in interdisciplinary research settingsPages 335 - 342
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  23. When less is more?Pages 343 - 350
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  24. From RAGs to rich responsesPages 353 - 368
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  25. The data interviewPages 369 - 382
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  26. Co‑creation of AI technology, empowering curators of cultural heritage information and guarding research commonsPages 383 - 412
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  27. AI‑Reporter: a path to a new genre of scientific communicationPages 413 - 438
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  28. Recursive reflectionsPages 439 - 450
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  29. Supporting citation context analysis with large language models raises questions that should have been asked 40 years agoPages 453 - 469
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  30. Reconstructive citation context analysis using large language modelsPages 471 - 492
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  31. Scaling In, Not Up?Pages 493 - 518
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