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Claim LD10-HQ-C23 (v1.1)

China-originating LLMs showed higher refusal rates, shorter responses and more inaccurate answers on 145 China-politics questions than non-China models; the study is observational and does not establish that regulation causes the gap.

Provenance and status

Claim type
ESTIMATE (as printed: “ESTIMATE”)
Publication status
ELIGIBLE (NON_CAUSAL) (as printed: “ELIGIBLE WITH NON-CAUSAL QUALIFIER”)
Verification state
CONTENT_VERIFIED_PRIMARY (as printed: “CONTENT VERIFIED”; verified by: UNKNOWN)
Does not license
Causal regulatory effect or downstream belief effect
Source (field 6, as printed)
Pan & Xu, PNAS Nexus 2026
Source type (field 7)
PEER_REVIEWED_JOURNAL_ARTICLE (as printed: “Peer-reviewed article”)
Institutional position
Independent academic
Maturity (claim evidence / publication maturity)
EMERGING (as printed: “EMERGING”)
Recorded date
2026-10-13
Currentness / correction
This is the current version. No correction or concern relation is recorded on its Sources.

Version

Pinned version v1.1 (entry LD10-HQ-C23--v1.1) — this page never floats to newer evidence.

This pinned version is also the current version.

Versions:v1.0v1.1 · current

What changed in this version

This version supersedes LD10-HQ-C23--v1.0, which stays available unchanged.

Changed fields
2 PUBLIC-FACING CLAIM; 25 RECORDED DATE
What changed
The public-facing claim now says the study does not establish that regulation causes the gap, instead of saying it does not identify regulation as the "sole" cause.
Why
A claim check on 13 October 2026 found that "not the sole cause" implies the study shows regulation to be a partial cause; the HQ primary read of Pan & Xu says the study explicitly does not establish a causal relationship between regulation and censorship behaviour. The check and this revision were done by AI agents against the HQ notes, not by a person.

Pinned Sources (field 6, version-specific)

Read the originals

All 25 canonical Claim fields (raw values and normalized values)
#Canonical fieldCanonical value (verbatim)Normalized (Claim Value Vocabulary v1.0.2)
1CLAIM IDLD10-HQ-C23—
2PUBLIC-FACING CLAIMChina-originating LLMs showed higher refusal rates, shorter responses and more inaccurate answers on 145 China-politics questions than non-China models; the study is observational and does not establish that regulation causes the gap.—
3CLAIM TYPEESTIMATEbase_types: ESTIMATE
4RESEARCH STRANDF — Platform/jurisdiction governance—
5MATURITYEMERGINGstates: EMERGING
6SOURCEPan & Xu, PNAS Nexus 2026source_ids: SRC-LD07-PAN-XU-CENSORSHIP-2026
7SOURCE TYPEPeer-reviewed articleclasses: PEER_REVIEWED_JOURNAL_ARTICLE
8INSTITUTIONAL POSITIONIndependent academic—
9SCOPEChinese- and English-language prompts; China-origin vs non-China models—
10DESIGNCross-sectional audit—
11SAMPLE / DATASET145 political questions plus less-sensitive set—
12MODEL / TOOL / VERSIONBaiChuan, ChatGLM, Ernie, DeepSeek vs Llama/GPT families—
13COMPARATORChina-origin vs non-China-origin models—
14MAIN RESULTLarge refusal/content differences; smaller gaps on less-sensitive prompts; all models refused more in Chinese—
15INFERENTIAL TARGETAssociation between provider origin/jurisdiction and model behaviour—
16DOES NOT LICENSECausal regulatory effect or downstream belief effect—
17VERIFICATION STATECONTENT VERIFIEDstate: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18LIMITATIONSObservational; model vintages; provider and technical differences—
19CONTRADICTORY / SUPPORTING SOURCESChinese regulatory framework is a plausible institutional condition—
20GEOGRAPHY / LANGUAGEPRC-origin vs non-PRC / Chinese and English—
21DISTRIBUTIONAL LEDGERChinese-language users received more refusals across models—
22R01 / FIELD-MAP LINKSD7, D6, D9, L-B, R-1—
23WHAT WOULD CHANGE THIS CLAIM?Newer-model longitudinal audit or causal design—
24PUBLICATION STATUSELIGIBLE WITH NON-CAUSAL QUALIFIERstate: ELIGIBLE · qualifiers: NON_CAUSAL
25RECORDED DATE2026-10-13—

Field 22 references resolved against Field Map v0.2: D7 (DOMAIN); D6 (DOMAIN); D9 (DOMAIN); L-B (LENS); R-1 (LENS)

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 42999ace40b1a0e064cbb5db742a1d6b8f14c5bcb7e542adeb96c85cf115b0d1.

Repository-authored inputs (not Drive exports)

  • SocioLens claim revision — LD10-HQ-C23 v1.1 (v1.1) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-13; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and docs/PROJECT.md "Research revisions" (a published claim changes only through a new version that states what changed and why). Block “ENTRY 23 — LD10-HQ-C23” SHA-256 b43928015cbcfda8409c2381df0b1f843facf02602e2c8540da96acbd2196f81; fixture fixtures/authored/claim-revisions/LD10-HQ-C23--v1.1.

Builder decisions (not Drive data)

  • VERSION = v1.1 — basis: Claim version taken from the repository-authored revision fixture version (SocioLens claim revision — LD10-HQ-C23 v1.1); a new version of LD10-HQ-C23 v1.0, which stays routable (AGENTS.md research integrity rule 3)
  • CURRENT_POINTER = true — basis: repository-authored revision "SocioLens claim revision — LD10-HQ-C23 v1.1" (Project Lead (Claude, AI), 2026-10-13); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and docs/PROJECT.md "Research revisions" (a published claim changes only through a new version that states what changed and why)
  • FIELD_6_SOURCE_REFERENCE = SRC-LD07-PAN-XU-CENSORSHIP-2026--v1.0 — basis: field 6 of ENTRY 23 — LD10-HQ-C23 cites "Pan & Xu, PNAS Nexus 2026"; the first field-6 segment of this ClaimVersion's repository-authored ledger block; the same Source record LD10-HQ-C23 v1.0 cites.
  • SOURCE_PROVENANCE_PIN = SRC-LD07-PAN-XU-CENSORSHIP-2026--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Pan & Xu, PNAS Nexus 2026".