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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1484816455
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zhenyue Zhao (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Inequality: 1 paper
- Innovation: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Other: 1 paper
- Research Productivity: 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 writing assistants are seeding academic papers with fake citations: an audit of 111 million references finds a surge in non-existent bibliographic entries after widespread LLM adoption, concentrated in fast-AI fields and among small or early-career teams. These hallucinated citations disproportionately credit already-prominent, male scholars and often survive moderation and peer review, risking biased and unreliable knowledge accumulation.arxiv | Zhenyue Zhao provider id |
2026-05-08 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.