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.

SocioLens project,

Claim LD09-SL-C02 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: strong scope
Verification state: Checked against the primary source. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: That AI causes fields to narrow, that the narrowing is harmful, or any finding about the social sciences
Limitations: Associational; the measures are embedding-based and depend on the model and sampling used; the paper’s own mechanism, data availability, is also the reason others give for treating concentration as rational; preliminary rerun for papers from the generative-AI era (in the arXiv version 4 supplement; the journal supplement was not read) is described as consistent but less significant
Source (as cited): Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026
Source type: Peer-reviewed journal article (recorded as “Peer-reviewed journal article”)
Institutional position: Independent academic (Tsinghua University, University of Chicago, Zhongguancun Academy and Santa Fe Institute)
Maturity of the evidence: Emerging evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026 (Peer-reviewed journal article)
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Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (STRONG_SCOPE) (as printed: “ELIGIBLE WITH STRONG SCOPE QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): PEER_REVIEWED_JOURNAL_ARTICLE
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD09-SL-C02	—
2	PUBLIC-FACING CLAIM	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.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Science and scholarship	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026	source_ids: SRC-SL-HAO-XU-LI-EVANS-NATURE-2026
7	SOURCE TYPE	Peer-reviewed journal article	classes: PEER_REVIEWED_JOURNAL_ARTICLE
8	INSTITUTIONAL POSITION	Independent academic (Tsinghua University, University of Chicago, Zhongguancun Academy and Santa Fe Institute)	—
9	SCOPE	Six natural sciences, 1980–2025	—
10	DESIGN	Text-embedding measures of the space covered by sampled sets of papers (knowledge extent) and of how often papers citing the same original paper cite each other (follow-on engagement)	—
11	SAMPLE / DATASET	41.3 million papers; AI and non-AI papers compared within discipline	—
12	MODEL / TOOL / VERSION	SPECTER 2.0 text embeddings; AI as a research method, as in LD09-SL-C01	—
13	COMPARATOR	Non-AI papers in the same disciplines and periods	—
14	MAIN RESULT	Knowledge extent 4.63% smaller for AI research, “consistent across all six disciplines”; 22% less follow-on engagement; AI paper families 3.46% more expanded than non-AI ones; topical relevance, original impact and funding priority “almost unrelated” to which areas adopt AI, while data availability “seems to be a major impacting factor”	—
15	INFERENTIAL TARGET	Whether AI-augmented research covers a narrower part of science than other research	—
16	DOES NOT LICENSE	That AI causes fields to narrow, that the narrowing is harmful, or any finding about the social sciences	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Associational; the measures are embedding-based and depend on the model and sampling used; the paper’s own mechanism, data availability, is also the reason others give for treating concentration as rational; preliminary rerun for papers from the generative-AI era (in the arXiv version 4 supplement; the journal supplement was not read) is described as consistent but less significant	—
19	CONTRADICTORY / SUPPORTING SOURCES	Individual portfolios broaden in the LLM era: LD09-SL-C07; precedent for narrowing with online search: LD09-SL-C03	—
20	GEOGRAPHY / LANGUAGE	Global / English	—
21	DISTRIBUTIONAL LEDGER	Areas rich in data attract AI work; areas without data may be left behind	—
22	R01 / FIELD-MAP LINKS	LD-09, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	A design separating data availability from AI use, or evidence that contraction reverses as AI spreads to data-poor areas	—
24	PUBLICATION STATUS	ELIGIBLE WITH STRONG SCOPE QUALIFIER	state: ELIGIBLE · qualifiers: STRONG_SCOPE
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026” → Hao et al. (2026), “Artificial intelligence tools expand…” (SRC-SL-HAO-XU-LI-EVANS-NATURE-2026--v1.0) — Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026; class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)

Cited by Living Debate versions

Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike? (v0.1) LD-09

Cite this page

SocioLens project. (2026, October 15). 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 [Claim LD09-SL-C02, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld09-sl-c02/v1.0/

@misc{sociolens_ld09_sl_c02_v1_0,
  author = {{SocioLens project}},
  title = {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.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld09-sl-c02/v1.0/}},
  note = {Claim LD09-SL-C02, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 d4db15d0bbbd4e4f6623caa8b6726a91fa8bf53ceac4e3effc3f7e97db0bde78.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD09-SL-C02 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts. Block “ENTRY 02 — LD09-SL-C02” SHA-256 9effc67017738002e0753911389e15fee59bfe4d087a3dfd1893758ce7c1fe34; fixture fixtures/authored/claims/LD09-SL-C02--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD09-SL-C02 v1.0); the first version of a new Claim of the LD-09 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD09-SL-C02 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-HAO-XU-LI-EVANS-NATURE-2026--v1.0 — basis: field 6 of ENTRY 02 — LD09-SL-C02 cites "Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-HAO-XU-LI-EVANS-NATURE-2026--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Hao, Xu, Li & Evans, “Artificial intelligence tools expand scientists’ impact but contract science’s focus,” Nature, 2026".