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:
2187317434
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Shahab Sharfaei (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Employment: 1 paper
- Innovation Output: 1 paper
- Job Displacement: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Recent empirical work finds little evidence of widespread AI-driven job loss; instead, newer AI tools, especially LLMs, often complement lower-skilled workers and help narrow productivity gaps with high-skilled staff.openalex | Shahab Sharfaei provider id |
2026-02-04 | 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.