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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2403192701
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ivan Smirnov (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
Claim outcomes
- Other: 1 paper
- Error Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Automatic sentiment and emotion classifiers misread men’s text more often than women’s: a study of over one million self-tagged posts finds higher error rates for male authors across 414 model–emotion combinations, including LLM-based tools, implying firms and researchers should not assume off-the-shelf detectors behave equitably when gender composition varies.arxiv | Ivan Smirnov provider id |
2026-01-08 | 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.