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
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2450899245
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yuanyuan Shen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Consumer Welfare: 1 paper
- Innovation Output: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Guaranteeing initial exploration views raises creator activity—about 8.6% more videos per creator and 7.1% more creators posting—yet short-window viewer A/B tests can miss the long-run benefit because the shared content corpus evolves slowly and requires long, isolated experiments to measure total value.arxiv | Yuanyuan Shen provider id |
2026-08-29 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.