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
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2384182975
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Surbhi Tiwari (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Innovation Output: 1 paper
- Other: 1 paper
- Ai Safety And Ethics: 1 paper
- Automation Exposure: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| Machine learning mostly increases output by improving predictions; deep learning pushes automation and capital intensity in skilled sectors; and Generative AI is shifting value toward cognitive augmentation, reshaping employment in knowledge industries.semantic_scholar | Surbhi Tiwari provider id |
Fetched 2026-06-11 | 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.