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
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2295670279
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Annie Liang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 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 |
|---|---|---|---|
| A legal test for generative-AI copying: an output infringes only if it could not have been produced without a specific training work, and under formal modeling this rule implies a sharp divide — when organic creation is light-tailed, individual dependence fades and regulation is unlikely to constrain generation, but with heavy-tailed creation regulation can remain binding.arxiv | Annie Liang provider id |
2026-02-12 | 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.