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
5Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5124861782
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ziang Xiao (openalex, 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
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Research Productivity: 1 paper
- Team Performance: 1 paper
- Firm Productivity: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 1 paper
- Error Rate: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Teams building LLM features use everything from informal 'vibe checks' to formal governance, but many cannot turn evaluation findings into concrete fixes — a persistent 'results-actionability gap' that slows LLM value capture; firms that invest in instrumentation and clear remediation pathways reap larger productivity gains.openalex | Ziang Xiao provider id |
2026-04-13 | 1 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.