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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5152406920
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Omar GUENNICH (openalex, provider refresh)
- Omar GUENNICH (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Governance And Regulation: 1 paper
- Wages: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| A semantic embedding analysis finds AI most aligned with high-skill cognitive tasks and high-wage professions — reading comprehension, writing and programming top the exposure list, and Education, ICT and Finance show the largest sectoral exposure; exposure correlates positively with salaries, challenging the view that automation chiefly threatens low-skill routine jobs.openalex | Omar GUENNICH provider id |
2026-09-21 | 0 |
Citation observation summary
OpenAlex 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.