Of about 49,800 papers at ten top AI conferences, about one in ten of those published from 2018 to 2022 acknowledged funding from large technology firms; 12% of these reached high citation rates, against 4% of papers with other funding and 2% of unfunded papers, and their reference lists were more insular.

SocioLens project,

Claim LD08-SL-C07 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: preprint
Verification state: Checked against the primary source; the source is a preprint. 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 industry funding causes higher citation, or anything about research on AI’s social effects
Limitations: Read in arXiv version 1; the arXiv record says it was published at an IEEE conference, which was not checked; NeurIPS is not among the ten conferences; funding type explains 13.3% of the variance in citations; funders were coded by one person; acknowledgements under-report funding and miss in-kind compute and model access, as the authors note
Source (as cited): Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025
Source type: Preprint (recorded as “Preprint”)
Institutional position: Independent academic (University of Göttingen)
Maturity of the evidence: Emerging evidence
Recorded date: 16 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025 (Preprint)
Read the original

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 (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (PREPRINT)
Source type (coded): PREPRINT
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD08-SL-C07	—
2	PUBLIC-FACING CLAIM	Of about 49,800 papers at ten top AI conferences, about one in ten of those published from 2018 to 2022 acknowledged funding from large technology firms; 12% of these reached high citation rates, against 4% of papers with other funding and 2% of unfunded papers, and their reference lists were more insular.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	A — Who produces knowledge about AI	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025	source_ids: SRC-SL-GNEWUCH-ET-AL-ARXIV-2512-05714
7	SOURCE TYPE	Preprint	classes: PREPRINT
8	INSTITUTIONAL POSITION	Independent academic (University of Göttingen)	—
9	SCOPE	Ten top AI conferences, 1998–2022, Scopus records	—
10	DESIGN	Coding of funding acknowledgements; citation analysis	—
11	SAMPLE / DATASET	About 49,800 papers; about 1.8 million outgoing and 2.3 million incoming citations	—
12	MODEL / TOOL / VERSION	Not applicable	—
13	COMPARATOR	Papers with non-industry funding and unfunded papers	—
14	MAIN RESULT	Industry-funded share below 2% in 2015, 11% in 2020, 9% in 2022; the source gives both 9% and 10% for 2018–2022; high citation rates 12% against 4% and 2%	—
15	INFERENTIAL TARGET	The scale and citation advantage of industry-funded AI research	—
16	DOES NOT LICENSE	That industry funding causes higher citation, or anything about research on AI’s social effects	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Read in arXiv version 1; the arXiv record says it was published at an IEEE conference, which was not checked; NeurIPS is not among the ten conferences; funding type explains 13.3% of the variance in citations; funders were coded by one person; acknowledgements under-report funding and miss in-kind compute and model access, as the authors note	—
19	CONTRADICTORY / SUPPORTING SOURCES	LD08-SL-C05	—
20	GEOGRAPHY / LANGUAGE	Global / English	—
21	DISTRIBUTIONAL LEDGER	Not analysed in this claim	—
22	R01 / FIELD-MAP LINKS	LD-08, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	Funding data from grant records rather than acknowledgements	—
24	PUBLICATION STATUS	ELIGIBLE WITH PREPRINT QUALIFIER	state: ELIGIBLE · qualifiers: PREPRINT
25	RECORDED DATE	2026-10-16	—

Field 22 references resolved against Field Map v0.2: LD-08 (LIVING_DEBATE); D3 (DOMAIN)

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

Pinned Sources (field 6, version-specific)

“Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025” → Gnewuch et al. (2025), “Big Tech-Funded AI Papers Have…” (SRC-SL-GNEWUCH-ET-AL-ARXIV-2512-05714--v1.0) — Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025; class PREPRINT (explicit public provenance pin)

Cited by Living Debate versions

Who can know about AI? Epistemic dependence and jurisdiction over the field (v0.1) LD-08

Cite this page

SocioLens project. (2026, October 16). Of about 49,800 papers at ten top AI conferences, about one in ten of those published from 2018 to 2022 acknowledged funding from large technology firms; 12% of these reached high citation rates, against 4% of papers with other funding and 2% of unfunded papers, and their reference lists were more insular [Claim LD08-SL-C07, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld08-sl-c07/v1.0/

@misc{sociolens_ld08_sl_c07_v1_0,
  author = {{SocioLens project}},
  title = {Of about 49,800 papers at ten top AI conferences, about one in ten of those published from 2018 to 2022 acknowledged funding from large technology firms; 12\% of these reached high citation rates, against 4\% of papers with other funding and 2\% of unfunded papers, and their reference lists were more insular.},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld08-sl-c07/v1.0/}},
  note = {Claim LD08-SL-C07, 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 512cf8fdf1e739d4c9245ac545f4ec0fc05bfdda34ac86c586e55c710df7d859.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD08-SL-C07 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-16; 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 16 October 2026 to build the LD-08 debate; a new Claim, first version, written from the project's LD-08 source notes, checked against full texts, official web pages or official abstracts. Block “ENTRY 07 — LD08-SL-C07” SHA-256 b0beba10d4af67b99eb58e3e7629d299054534b7c779cdc175638b8dde2d996e; fixture fixtures/authored/claims/LD08-SL-C07--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD08-SL-C07 v1.0); the first version of a new Claim of the LD-08 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD08-SL-C07 v1.0" (Project Lead (Claude, AI), 2026-10-16); 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 16 October 2026 to build the LD-08 debate; a new Claim, first version, written from the project's LD-08 source notes, checked against full texts, official web pages or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-GNEWUCH-ET-AL-ARXIV-2512-05714--v1.0 — basis: field 6 of ENTRY 07 — LD08-SL-C07 cites "Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-GNEWUCH-ET-AL-ARXIV-2512-05714--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Gnewuch, Wahle, Ruas & Gipp, “Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias,” arXiv:2512.05714, 2025".