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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5124036951
ORCID evidence
No valid ORCID is stored.
Observed aliases (4)
- Juan Wu (openalex, provider refresh)
- Juan Wu (openalex, source metadata)
- Wu, Juan (openalex, provider refresh)
- Wu, Juan (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Output Quality: 1 paper
- Wages: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Mandatory labeling of AI-generated content raises transparency but cuts creator surplus and can stifle high-quality outputs; platforms should move from strict policing to lighter screening as AI capabilities increase.openalex | Wu, Juan provider id |
2026-01-26 | 0 |
Citation observation summary
OpenAlex 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.