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
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
40412475
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Stewart Jones (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Job Displacement: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| Machine learning is transforming accounting: models beat traditional methods in prediction tasks and incorporate new data sources, challenging accountants' role as the primary arbiters of judgement and forcing firms to develop new governance and skills.openalex | Stewart Jones provider id |
2026-02-23 | 6 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 6 cumulative citations. This is a coverage summary, not an author score or h-index.