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Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams — arXiv:2609.14425

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14425
License (as archived): CC0-1.0
Archived: 2026-10-15T22:51:05.081Z
SHA-256 of the archived text: da97b73691a682b279b0a6eb7c4ae4862da0572438281839508655747f8b3c16
Stable URL of this page: https://metraipolis.com/documento/dc_99caefbc3bc5

How to cite

Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams — arXiv:2609.14425 — arXiv (en), https://arxiv.org/abs/2609.14425 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_99caefbc3bc5 (Metraipolis archive dc_99caefbc3bc5, sha256 da97b73691a682b279b0a6eb7c4ae4862da0572438281839508655747f8b3c16)

BibTeX

@misc{dc99caefbc3bc5,
  title = {Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams — arXiv:2609.14425},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14425},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_99caefbc3bc5; sha256 da97b73691a682b279b0a6eb7c4ae4862da0572438281839508655747f8b3c16},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14425. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative artificial intelligence raises a central question for scientific training and organization. Is research shifting from deep specialization toward broad individual knowledge? We examine this proposition across papers, cited knowledge, contributor histories, and teams using 47,959 articles from six fields over 2010-2025, 51,736 resolved cited works, and chronologically reconstructed prior publication histories for 1,754 randomly selected index contributors. From 2010 to 2022, team size increased by an estimated 37.3% (95% confidence interval [34.4%, 40.3%]), while paper topic breadth declined by 0.0144 on a 0-1 hierarchical distance scale. Cited knowledge was stable to modestly broader, revealing a divergence between focused outputs and the reach of knowledge inputs. Established contributors' prior breadth increased by 0.0190 [-0.0078, 0.0459] by 2019-2022, within a +/-0.05 equivalence bound assessed in sensitivity analysis. In mature citation windows, one standard deviation of focal depth was associated with 8.2% higher 1 + FWCI [1.9%, 14.9%]; average breadth and interaction associations were smaller under the specified equivalence bounds. Post-2022 deviations from earlier trends were not systematic, and recent changes did not vary clearly with baseline AI intensity across 83 subfields. The findings support a differentiated structure of scientific expertise in which focused individual accumulation coexists with expanding collaboration and sustained access to diverse knowledge inputs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Digital Libraries (cs.DL) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.14425 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.666Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).