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
187Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2376697848
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- A. Hagenah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Consumer Welfare: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Market Structure: 1 paper
- Social Protection: 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 experts see multiple pathways to catastrophe within five years: in a Delphi of 272 specialists, 18 of 24 assessed risks had more than a 10% chance of catastrophic outcomes under business‑as‑usual by 2030, and even with pragmatic mitigations five risks retained >10% catastrophe probability, with general‑purpose AI developers and governance actors judged most responsible for mitigation.arxiv | A. Hagenah provider id |
2026-06-03 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.