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Proposal pack · RO-LD09-01 v1

Does language-model use narrow what whole fields study, including the social sciences?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-09 debate on 15 October 2026, not a finding. Not yet reviewed. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld09-01/v1/.

Not yet measured A PhD project Low to medium cost Partly addressed

Research question

Since language models became widely used in 2023, has the range of topics covered by papers from adopting researchers shrunk relative to non-adopters, at the level of whole fields, and does the answer differ between the natural and the social sciences?

Why it matters

The strongest field-level evidence of narrowing comes from AI used as a research method in six natural sciences, mostly before chat assistants, and its authors exclude the social sciences. Studies of the language-model era find adopters drawing on more diverse prior work and scientists in general spreading across more fields, but they measure individuals, not the space a field covers. Without the field-level measure for language-model use, the debate cannot say whether the narrowing position (P1) or the concentration position (P2) describes the present.

The debate lists this among what we still do not know: Does language-model use narrow what fields study, measured as section 1 measured AI methods in the natural sciences?

Evidence that motivates it

  1. In the same natural-science corpus, AI-augmented research was associated with a 4.63% smaller median collective knowledge extent and 22% less follow-on engagement among papers citing the same work, while each AI paper’s own family of citing papers was 3.46% more spread out; the authors link the contraction to AI work clustering where data are abundant. (LD09-SL-C02 v1.0)
    AI-augmented natural-science research covered a 4.63% smaller knowledge extent, associated with data availability; the authors exclude the social sciences.
    Cites: Hao et al. (2026), “Artificial intelligence tools expand…”
  2. Across arXiv, bioRxiv and SSRN preprints (2018–2024), authors whose abstracts first showed signs of language-model writing then posted 36.2%, 52.9% and 59.8% more papers respectively; for their manuscripts, more complex writing went with lower odds of publication, the reverse of the pattern for human-written ones; and adopters drew on “more diverse prior work”, being 11.9% more likely to cite books and citing younger documents. (LD09-SL-C05 v1.0)
    Language-model adopters posted more preprints, were more likely to cite books and cited younger documents.
    Cites: Kusumegi et al. (2025), “Scientific production in the era…”
  3. Among 775,323 scientists with full-text papers in PubMed Central, individual research portfolios spread across more fields after 2022: field entropy rose from 0.55 in 2022 to 0.71 in 2025 and the share entering a new field from 30.4% to 35.5%, more so for scientists in non-English-speaking low- and middle-income countries; the authors call the design descriptive. (LD09-SL-C07 v1.0)
    Individual portfolios spread across more fields after 2022, in a descriptive design.
    Cites: Zheng et al. (2026), “Scientific exploration…”
  4. By February 2024 an estimated 17.5% of sentences in computer-science abstracts on arXiv were substantially modified by language models; the share was higher in papers closer to their nearest peer, a proxy for more crowded research areas (22.2% against 14.7% for less similar papers). (LD09-SL-C09 v1.0)
    Language-model-modified sentences were more common in crowded research areas.
    Cites: Liang et al. (2024), “Mapping the Increasing Use of LLMs…”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Classify papers in open full-text and abstract corpora (arXiv, bioRxiv, SSRN, PubMed Central and social-science repositories) by estimated language-model use from 2018 on, using a distribution-level estimator rather than per-paper detection. Apply the knowledge-extent and follow-on-engagement measures of the natural-science study to adopters and non-adopters within field and year, adding economics, sociology, psychology and political science. Exploit differences in the timing of institutional access to language models (for example national blocks or licence roll-outs) for a difference-in-differences design, and test whether data availability explains any contraction.

Data needed

Open abstract and full-text corpora with field codes; a validated distribution-level estimator of language-model text; dates of institutional or national access to chat assistants; text embeddings frozen at one version.

Feasibility

A PhD project. Low to medium; the corpora are public and the measures need modest compute, but validating the estimator for social-science writing needs hand-labelled samples. Per-paper detection is unreliable, so adoption must be estimated at group level; dating adoption by first flagged paper creates artefacts (LD09-SL-C06) and should be avoided.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 15 October 2026; not yet checked in depth).

No study was found that applies field-level breadth measures to language-model use or to the social sciences. Nearest work measures article-level novelty after 2022 in one field without observing model use, the distinctiveness of individual grant proposals, the prevalence of model-modified text by field, or individual scientists' field-switching.

Where the search could not reach. Screened by web search (3 queries) and arXiv queries on the date shown; Scopus, Web of Science and Dimensions were not searched, and science-of-science conference proceedings were not checked. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld09_01_v1,
  author = {{SocioLens project}},
  title = {Does language-model use narrow what whole fields study, including the social sciences?},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld09-01/v1/}},
  note = {Research opportunity RO-LD09-01, version v1}
}

@misc{ro_ld09_01_nearest_1,
  title = {Qian, Wen, Furnas, Bai, Shao \& Wang, "The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding," arXiv:2601.15485 (2026); published as PNAS 123(33), e2601439123 (2026), per the Crossref record},
  url = {https://arxiv.org/abs/2601.15485},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_01_nearest_2,
  title = {Kousha \& Thelwall, "How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis," arXiv:2509.09596 (2025)},
  url = {https://arxiv.org/abs/2509.09596},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_01_nearest_3,
  title = {Cao, "Visualizing the adoption of large language models across sociology subfields," Socius (2026), doi 10.1177/23780231261460657},
  url = {https://doi.org/10.1177/23780231261460657},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld09_01_nearest_4,
  title = {Zheng et al., PubMed Central study of field-switching (LD09-SL-C07)},
  url = {https://arxiv.org/abs/2607.20923},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld09_01_nearest_5,
  title = {Safari \& Babaei, "Research Novelty in Information Systems Journals After ChatGPT: Differences Across Institutional Language Contexts," arXiv:2603.22510 (2026)},
  url = {https://arxiv.org/abs/2603.22510},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_hao_xu_li_evans_nature_2026,
  title = {Hao, Xu, Li \& Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026},
  url = {https://doi.org/10.1038/s41586-025-09922-y},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_kusumegi_et_al_science_2025,
  title = {Kusumegi, Yang, Ginsparg, de Vaan, Stuart \& Yin, “Scientific production in the era of large language models,” Science 390, 2025 (author version arXiv:2601.13187)},
  url = {https://doi.org/10.1126/science.adw3000},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_zheng_hong_liu_ni_arxiv_2607_20923,
  title = {Zheng, Hong, Liu \& Ni, “Scientific exploration, collaboration and labor division in the large language model era,” arXiv:2607.20923, July 2026},
  url = {https://arxiv.org/abs/2607.20923},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_liang_et_al_arxiv_2404_01268,
  title = {Liang, Zhang, Wu, Lepp, Ji, Zhao et al., “Mapping the Increasing Use of LLMs in Scientific Papers,” arXiv:2404.01268, April 2024},
  url = {https://arxiv.org/abs/2404.01268},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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