Claim LD09-SL-C08 (v1.0)
Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit.
Provenance and status
- Claim type
- ESTIMATE (as printed: “ESTIMATE”)
- Publication status
- ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
- Verification state
- CONTENT_VERIFIED_PRIMARY (PREPRINT) (as printed: “CONTENT VERIFIED PREPRINT”; verified by: UNKNOWN)
- Does not license
- Any measure of topic narrowing, or a causal effect of AI use
- Source (field 6, as printed)
- Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)
- Source type (field 7)
- PEER_REVIEWED_JOURNAL_ARTICLE (as printed: “Peer-reviewed journal article (read in preprint version)”)
- Institutional position
- Independent academic (Northwestern University)
- Maturity (claim evidence / publication maturity)
- EMERGING (as printed: “EMERGING”)
- Recorded date
- 2026-10-15
- Currentness / correction
- This is the current version. No correction or concern relation is recorded on its Sources.
Version
Current version v1.0, resolved through the explicit current pointer CLAIM--LD09-SL-C08.
Pinned Sources (field 6, version-specific)
- “Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)” → SRC-SL-GAO-WANG-NHB-2024--v1.0 — Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578); class PEER_REVIEWED_JOURNAL_ARTICLE (explicit public provenance pin)
All 25 canonical Claim fields (raw values and normalized values)
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD09-SL-C08 | — |
| 2 | PUBLIC-FACING CLAIM | Across 74.6 million publications from 1960 to 2019, papers that use AI were more likely to be among the top 5% most cited in their field and year for a majority of disciplines, a premium the authors find stronger in disciplines with a lower propensity to use AI, while disciplines with more women or Black scientists showed less potential benefit. | — |
| 3 | CLAIM TYPE | ESTIMATE | base_types: ESTIMATE |
| 4 | RESEARCH STRAND | A — Science and scholarship | — |
| 5 | MATURITY | EMERGING | states: EMERGING |
| 6 | SOURCE | Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578) | source_ids: SRC-SL-GAO-WANG-NHB-2024 |
| 7 | SOURCE TYPE | Peer-reviewed journal article (read in preprint version) | classes: PEER_REVIEWED_JOURNAL_ARTICLE |
| 8 | INSTITUTIONAL POSITION | Independent academic (Northwestern University) | — |
| 9 | SCOPE | Science and scholarship broadly (19 disciplines, including the social sciences), publications 1960–2019 | — |
| 10 | DESIGN | Text-based measurement of AI use across publications and patents; comparison of citation outcomes | — |
| 11 | SAMPLE / DATASET | 74.6 million publications and 7.1 million patents | — |
| 12 | MODEL / TOOL / VERSION | AI use identified from AI terms (n-grams) in titles and abstracts, with AI papers defined by Microsoft Academic Graph field categories | — |
| 13 | COMPARATOR | Papers in the same field and year that do not use AI | — |
| 14 | MAIN RESULT | A citation premium for AI-using papers (higher chance of being in the top 5% by citations) for a majority of disciplines, appearing stronger where AI use is less common; less potential benefit where women or Black scientists are better represented | — |
| 15 | INFERENTIAL TARGET | Who gains from the use of AI in research | — |
| 16 | DOES NOT LICENSE | Any measure of topic narrowing, or a causal effect of AI use | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED PREPRINT | state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Read in the preprint (arXiv version 2, under an earlier title), not the Nature Human Behaviour version; the journal details come from the Crossref record; AI use identified from field categories and text terms; associational | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | Individual gains in natural science: LD09-SL-C01 | — |
| 20 | GEOGRAPHY / LANGUAGE | Global / English | — |
| 21 | DISTRIBUTIONAL LEDGER | Potential benefit lower in disciplines with more women or Black scientists | — |
| 22 | R01 / FIELD-MAP LINKS | LD-09, D3 | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Evidence on who adopts AI within disciplines and with what returns | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH QUALIFIER | state: ELIGIBLE · qualifiers: QUALIFIED |
| 25 | RECORDED DATE | 2026-10-15 | — |
Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)
Cited by Living Debate versions
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 bb8959b46937f86ebd4ffa7c584c20afe43f194765a3bf3d64ecc333fab4478f.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD09-SL-C08 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 08 — LD09-SL-C08” SHA-256
88a5c153c01c2999e5cf0dba1b56dca1aaf0cdf7b7457f4fc5442145fa73298a; fixturefixtures/authored/claims/LD09-SL-C08--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-C08 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-C08 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-GAO-WANG-NHB-2024--v1.0 — basis: field 6 of ENTRY 08 — LD09-SL-C08 cites "Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-GAO-WANG-NHB-2024--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Gao & Wang, “Quantifying the use and potential benefits of artificial intelligence in scientific research,” Nature Human Behaviour 8, 2024 (arXiv:2304.10578)".