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:
2425014532
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Sivaprakash Sunkara (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Market Structure: 1 paper
- Regulatory Compliance: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Employment: 1 paper
- Error Rate: 1 paper
- Job Displacement: 1 paper
- Other: 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 governed hyperautomation pattern—combining low-code, RPA and generative AI with embedded policy, human‑in‑the‑loop checks and continuous monitoring—lets firms scale automation without sacrificing compliance or stability; the approach raises upfront governance costs but can lower risk‑adjusted total cost of ownership and reshape labor demand toward oversight and AI‑engineering roles.openalex | Sivaprakash Sunkara provider id |
2026-03-06 | 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.