Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
47112979
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Wenxiu Li (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Innovation Output: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Labeling products or strategy with 'AI' without substance appears to hurt green innovation: Chinese listed firms that AI-wash their annual reports show lower green patenting, a decline transmitted through reputation losses in product markets and increased financing constraints. The effect is strongest for private firms, SMEs and firms in highly competitive sectors, suggesting targeted disclosure and regulatory measures could restore market incentives.arxiv | Wenxiu Li provider id |
2026-03-19 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.