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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2387215808
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yutang Guan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Automation Exposure: 1 paper
- Consumer Welfare: 1 paper
- Firm Productivity: 1 paper
- Labor Share: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| Embodied AI raises productivity but deepens power imbalances: algorithms displace worker decision-making and extract uncompensated behavioral data, reinforcing monopoly capital and unequal access to technological dividends; coordinated institutional, organizational, and individual rights reforms are needed to rebalance human–machine production relations.openalex | Yutang Guan provider id |
2026-06-15 | 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.