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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2352686465
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Marina Mendes Tavares (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Employment: 1 paper
- Skill Acquisition: 1 paper
- Wages: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| AI and other new digital skills are appearing in roughly one in ten vacancies in advanced economies and pay a clear wage premium. Yet their spread is linked to sharper labor-market polarization—helping high-skilled workers while hollowing out middle-skilled roles and reducing employment in AI‑exposed occupations with low worker complementarity, with young workers hit especially hard.semantic_scholar | Marina Mendes Tavares provider id |
Fetched 2026-03-10 | 10 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 10 cumulative citations. This is a coverage summary, not an author score or h-index.