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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2336246737
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hao He (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Developer Productivity: 1 paper
- Task Allocation: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| A single firm's AI coding push coincided with a doubling of developers' merged pull requests to 2.09x pre-mandate levels by April 2026; evidence links most of the gain to voluntary adoption and accumulated use, and code-review work shifted heavily toward automation without higher revert rates.openalex | Hao He provider id |
2026-07-02 | 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.