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 →
2Distinct papers
10Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2153402989
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Santanu Bhattacharya (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 2 papers
- Automation Exposure: 2 papers
- Governance And Regulation: 1 paper
- Labor Share: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Research Productivity: 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 large procurement workflow that looks safe at the state level can still hide sizable next-step uncertainty: expanding state detail from 42 to 668 raises state-action blind mass from 0.0165 to 0.1253, implying much higher oversight needs than coarse metrics suggest; a simple maximum-probability score m(s) predicted autonomous-step accuracy on held-out data within about 3.4 percentage points.arxiv | Santanu Bhattacharya provider id |
2026-03-25 | 0 |
| A skills-focused simulation suggests AI’s technical reach in the U.S. labor market is far larger and more geographically widespread than visible adoption indicates: current AI capabilities overlap with about 11.7% of U.S. wages (~$1.2tn), compared with roughly 2.2% (~$211bn) reflected in present adoption concentrated in tech hubs.semantic_scholar | Santanu Bhattacharya provider id |
Fetched 2026-03-10 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.