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 →
2Distinct papers
2Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2276836947
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Pawel Niszczota (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 2 papers
- Governance: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Wages: 1 paper
- Decision Quality: 1 paper
- Research Productivity: 1 paper
- Ai Safety And Ethics: 1 paper
- Other: 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 |
|---|---|---|---|
| Peers financially punish LLM users: in an online experiment, participants destroyed on average 36% of the earnings of workers who relied solely on an LLM, and punishment increased with extent of use; curious asymmetries emerged as denials of use were treated with particular suspicion.arxiv | Pawel Niszczota provider id |
2026-01-14 | 1 |
| People judge machine-made offers as less socially appropriate and are more willing to reject them, yet accept machine-enforced rejections as no less appropriate than human ones; in short, machines are held to different fairness norms for decisions but not for enforcement.arxiv | Pawel Niszczota provider id |
2026-01-14 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.