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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
3472031
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tatiana Codreanu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Adoption Rate: 1 paper
- Team Performance: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| Human-AI cooperation succeeds only when institutions distribute residual risk and align incentives; a formal model and six design principles show how governance, accountability regimes, and visible trust infrastructure turn AI collaboration from a structural defect into a sustainable institutional achievement.arxiv | Tatiana Codreanu provider id |
2026-02-23 | 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.