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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2454710850
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xinwen Zhang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Other: 1 paper
- Automation Exposure: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Job Displacement: 1 paper
- Skill Acquisition: 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 has reshaped labor markets unevenly: while productivity and output gains are common, benefits are highly concentrated—fueling factor‑income polarization, rising skill demands, and elevated displacement risks, with much of the evidence coming from a few lead economies.openalex | Xinwen Zhang provider id |
2026-07-27 | 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.