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
20Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2497637147
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Civic-Ai Collaboration Jiaying Wu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Hiring: 1 paper
- Organizational Efficiency: 1 paper
- Skill Acquisition: 1 paper
- Skill Obsolescence: 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 |
|---|---|---|---|
| AI only augments work when entire workflows—not just isolated tasks—are redesigned to deliver durable net value, preserve meaningful human control and accountability, and sustain learning and career pathways; otherwise short-term productivity gains risk eroding oversight, skills, and job quality.arxiv | Civic-Ai Collaboration Jiaying Wu provider id |
2026-09-11 | 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.