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:
2146275009
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yifang Ma (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Regulatory Compliance: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 1 paper
- Skill Acquisition: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Generative AI can sharply raise efficiency and accuracy in corporate finance and tax operations, cutting routine costs and freeing staff for higher‑value work; but realising these gains at scale hinges on addressing data privacy, model reliability, system integration and regulatory accountability.openalex | Yifang Ma provider id |
2026-03-13 | 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.