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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2397182270
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Umar Yunis-Guerra (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Task Allocation: 1 paper
- Firm Productivity: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Task-level analysis of 193,497 UK Civil Service vacancies finds wide variation in AI exposure even within identical job titles; LLM-driven redesigns point to augmentation and productivity gains—strategic leadership, complex problem-solving and stakeholder management remain human strengths rather than roles being broadly automated.arxiv | Umar Yunis-Guerra provider id |
2025-12-05 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.