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:
2377422587
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Emmanuel Peprah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Audits still miss material fraud despite tighter rules; integrating behavioural reforms, stronger incentives and AI analytics is necessary to narrow the gap, though tech adoption risks concentrate audit quality and raise governance challenges.openalex | Emmanuel Peprah provider id |
2026-08-14 | 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.