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:
2278567690
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jia-Hua Zhao (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Labor Share: 1 paper
- Firm Productivity: 1 paper
- Inequality: 1 paper
- Other: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| AI nudges firms downstream in global value chains and makes technology more labor-augmenting, lifting workers' share and narrowing internal pay gaps but squeezing corporate profits; effects are most pronounced in private firms and when AI hardware platforms are deployed.openalex | Jia-Hua Zhao provider id |
2026-09-16 | 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.