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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5002720299
ORCID evidence
Observed aliases (2)
- Luca Pieroni (openalex, provider refresh)
- Luca Pieroni (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
Claim outcomes
- Employment: 1 paper
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Labor Share: 1 paper
- Skill Acquisition: 1 paper
- Wages: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Federal agencies with more AI-exposed jobs are shifting staff from routine to expert roles while compressing wages, suggesting reallocation under public-sector constraints rather than mass layoffs. The pattern reflects institutional features—employment protections, standardised pay, and political oversight—that shape how technology-driven change plays out in government.openalex | Luca Pieroni provider id |
2026-01-01 | 0 |
Citation observation summary
OpenAlex 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.