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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5128959862
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Deborah Osahor (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Innovation Output: 1 paper
- Regulatory Compliance: 1 paper
- Research Productivity: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| A hybrid machine-learning and blockchain accounting prototype boosts fraud detection and slashes reconciliation times in pilot datasets — but scalability, privacy and transparency remain barriers to wider rollout.openalex | Deborah Osahor provider id |
2026-03-13 | 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.