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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2368891259
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Simeon Nsabiyumva (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Governance And Regulation: 1 paper
- Inequality: 1 paper
- Job Displacement: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| GenAI may not trigger mass job losses in low-income countries, but it risks deepening capability inequality as data, compute and rule‑making remain concentrated in the Global North; investing in data sovereignty, labour‑augmenting applications and Global South governance could steer technology toward decent work.openalex | Simeon Nsabiyumva provider id |
2025-12-11 | 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.