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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2085914242
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- I. Shehu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Fiscal And Macroeconomic: 1 paper
- Inequality: 1 paper
- Adoption Rate: 1 paper
- Employment: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Labor Share: 1 paper
- Research Productivity: 1 paper
- Social Protection: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Automation and AI are tilting production toward technological capital, shrinking labour’s share and eroding payroll‑based revenues; policymakers must rethink tax bases and pension designs or face mounting fiscal and social strain.semantic_scholar | I. Shehu provider id |
Fetched 2026-03-10 | 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.