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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2308035434
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Richard Hawkins (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
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| To safely replace constrained human roles with LLMs in critical information flows, evaluate LLM outputs with a basket of weighted LLM-as-Judge metrics and require human review when evaluators disagree. The paper outlines a practical, evaluation-focused safety framework rather than new model techniques, but offers no empirical validation or calibration guidance for real-world deployment.arxiv | Richard Hawkins provider id |
2025-12-17 | 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.