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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2402445561
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tingxi Yan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Developer Productivity: 1 paper
- Employment: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Hiring: 1 paper
- Market Structure: 1 paper
- Task Allocation: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Generative AI reshapes 2D animation by automating modular asset work and elevating supervisory, pipeline, and compliance roles; gains in iteration speed coexist with compressed entry-level pay and tougher provenance and copyright constraints.openalex | Tingxi Yan provider id |
2025-12-31 | 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.