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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2438637879
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Sayali Nipane (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Employment: 1 paper
- Consumer Welfare: 1 paper
- Inequality: 1 paper
- Innovation Output: 1 paper
- Job Displacement: 1 paper
- Organizational Efficiency: 1 paper
- Skill Obsolescence: 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 |
|---|---|---|---|
| AI is reshaping jobs: it automates routine tasks while spawning new roles, but the net effect on employment and inequality depends on reskilling, firm practices and policy; without deliberate workforce investment and governance, displacement and rising inequality are likely.openalex | Sayali Nipane provider id |
2026-05-23 | 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.