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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2267717
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Krishnadas (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Innovation Output: 1 paper
- Ai Safety And Ethics: 1 paper
- Developer Productivity: 1 paper
- Hiring: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 1 paper
- Turnover: 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 |
|---|---|---|---|
| An AI-driven talent-management rollout in one software firm is reported to have raised productivity by about a third and nearly halved innovation failures, while cutting attrition and hiring bias — but the evidence comes from a single before-and-after evaluation with limited methodological transparency.openalex | S. Krishnadas provider id |
2025-12-20 | 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.