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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2818759
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Marzieh Fadaee (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 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 |
|---|---|---|---|
| Static 'GPTs are GPTs' exposure scores have become a convenient proxy for AI’s labour risks, but they were never designed for policy decisions; relying on them without dynamic, usage and worker-centered measurement risks misleading who will be affected and when. Fixing this requires new measurement infrastructure, participatory research, and sustained coordination between researchers and policymakers.arxiv | Marzieh Fadaee provider id |
2026-06-22 | 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.