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
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2391242432
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Nina Xie (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Social Protection: 1 paper
- Training Effectiveness: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Redesigning mandatory pre‑departure training in South–South corridors can cut brokerage rents and raise migrants' employability by delivering earlier, decentralized, and TVET‑aligned learning with portable credentials. Generative AI can serve as low‑cost, multilingual learning support, but must be limited to auditable, assistive roles to avoid becoming a hidden gatekeeper.openalex | Nina Xie provider id |
2026-03-09 | 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.