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
1/1OpenAlex citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5128685083
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Neuman, W. Russell (openalex, provider refresh)
- W. Russell Neuman (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Large language models can serve as cheap, large-scale instruments for measuring cultural norms, beliefs and narratives—by probing pretrained weights economists can approximate aggregate discourse—yet alignment, fine-tuning and sampling choices can distort those signals, so researchers should prefer base or minimally adapted models, validate against surveys, and publish provenance and prompts.semantic_scholar | Neuman, W. Russell provider id |
Fetched 2026-03-15 | 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.