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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2272249584
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yingxin Ma (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Fiscal And Macroeconomic: 1 paper
- Firm Productivity: 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 |
|---|---|---|---|
| Chinese industries with concentrated AI patenting grew roughly 1.9 percentage points faster after 2010 than low‑AI sectors, with the biggest gains in high‑tech, capital‑ and knowledge‑intensive industries; the effect rises over time and is amplified by R&D, though measurement and endogeneity concerns limit definitive causal claims.openalex | Yingxin Ma provider id |
2026-03-13 | 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.