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:
A5125296687
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Di Wang (openalex, provider refresh)
- Di Wang (openalex, source metadata)
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
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 1 paper
- Task Allocation: 1 paper
- Team Performance: 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 |
|---|---|---|---|
| AI 'speedup' claims are often non‑comparable and misleading because they ignore quality tradeoffs, rework, and integration costs; the authors propose measuring productivity as Time‑To‑Acceptance under documented acceptance tests. They introduce a quantitative framework and a lightweight Human‑AI Productivity Card to normalize task complexity, capture uncertainty and rework, and standardize reporting across studies.openalex | Di Wang provider id |
2026-02-06 | 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.