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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2054842299
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Feiyang Kang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
Claim outcomes
- Market Structure: 1 paper
- Research Productivity: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Labor Share: 1 paper
- Wages: 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 |
|---|---|---|---|
| Public data deals channel most value to aggregators while creators receive negligible royalties, exposing a structural data-value inequality; missing provenance, uneven bargaining power and static pricing threaten the sustainability of current ML pipelines and prompt a proposed EDVEX framework to redistribute benefits.openalex | Feiyang Kang provider id |
2026-01-15 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.