---
title: "Modeling Changing Scientific Concepts with — Valdez 2026"
description: "The paper addresses a weakness of LLM context embeddings for tracing conceptual change: they are hard to interpret and do not encode time."
authors: ["Sofia Aguilar Valdez", "Stefania Degaetano-Ortlieb"]
year: 2026
venue: "Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026"
source: "https://aclanthology.org/2026.latechclfl-1.14/"
doi: "10.18653/v1/2026.latechclfl-1.14"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/valdez-2026-modeling-changing-scientific-concepts-complex/"
---
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# Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution

**Authors:** Sofia Aguilar Valdez, [Stefania Degaetano-Ortlieb](https://md.research.elamur.ai/free/en/authors/stefania-degaetano-ortlieb/) · **Year:** 2026 · **Venue:** Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.latechclfl-1.14/) · [PDF](https://aclanthology.org/2026.latechclfl-1.14.pdf) · [DOI 10.18653/v1/2026.latechclfl-1.14](https://doi.org/10.18653/v1/2026.latechclfl-1.14) · [OpenAlex](https://openalex.org/W7140103623)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary

The paper addresses a weakness of LLM context embeddings for tracing conceptual change: they are hard to interpret and do not encode time. The authors propose an interpretable alternative grounded in prototype semantics. A concept is treated as a structure with core and peripheral readings, and the task is framed as "conceptual structure shift detection."

They draw decades of texts from the 1750s to the 1800s from the Royal Society Corpus (RSC), filtered with oxygen-related terminology. Topic modeling turns themes into ranked vocabularies, and documents are clustered by similarity into decade-level networks. Change is measured with Jensen-Shannon distance, entropy (as topic diversity) and network statistics.

In the case study of phlogiston versus oxygen theory, one topic cluster moves its core from *air* to *acid*. Over the same period, *calx* and *phlogiston* drop out and *oxygen* appears. This onomasiological change coincides with rising entropy and denser, less modular graphs, which the authors read as greater diversity of ideas and more effort needed to connect them. The framework gives digital humanities researchers an inspectable, time-aware view of shifts in scientific vocabulary.

## Key points

- Data come from the Royal Society Corpus, covering the 1750s–1800s. Only English texts are included, so Lavoisier's French writings are missing.
- A 6-topic model was chosen because it had low perplexity and high coherence across decades. Cumulative and non-cumulative sampling were compared.
- Entropy behaves differently by strategy. With cumulative sampling it keeps rising. With non-cumulative sampling it falls before about 1780 and then rises, which the authors link to phlogistonist resistance followed by Lavoisier's new terminology.
- Six decade graphs were built. Node counts stay around 400–500, while edges roughly double (30,037 → 62,639). Communities drop from 5 to 3 and modularity from 0.39 to 0.19. The percolation threshold moves 0.3 → 0.54 → 0.32 → 0.58.
- Planned extensions include directed edges, better handling of rare terms, phylogenetic structures, and tests in other fields, such as the biological transition from the idea of germ plasm to that of genes located on chromosomes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [History & humanities](https://md.research.elamur.ai/free/en/shelves/history/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{valdez2026modeling,
  title = {{Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution}},
  author = {Valdez, Sofia Aguilar and Degaetano-Ortlieb, Stefania},
  booktitle = {Proceedings of the 10th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026},
  year = {2026},
  doi = {10.18653/v1/2026.latechclfl-1.14},
  url = {https://doi.org/10.18653/v1/2026.latechclfl-1.14}
}
```

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48. Figure A1: Temporal graphs. The colors illustrate the number of communities which went on decline: starting at 5 (1750s) and ending at 3 (1800s). [find](https://scholar.google.com/scholar?q=Figure+A1%3A+Temporal+graphs%2E+The+colors+illustrate+the+number+of+communities+which+went+on+decline%3A+starting+at+5+%281750s%29+and+ending+at+3+%281800s%29%2E)
49. Figure A2: Network metrics. We used five parameters to interpret network stability over time: nodes size, edge density (where y = 1 × 10 3 , e.g., last number of edges reported is \>60 000), communities count, modularity and percolation threshold. [find](https://scholar.google.com/scholar?q=Figure+A2%3A+Network+metrics%2E+We+used+five+parameters+to+interpret+network+stability+over+time%3A+nodes+size%2C+edge+density+%28where+y+%3D+1+%C3%97+10+3+%2C+e%2Eg%2E%2C+last+number+of+edges+reported+is+%5C%3E60+000%29%2C+communiti)