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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5137910071
ORCID evidence
Observed aliases (1)
- Yasmina El Fassi (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Employment: 1 paper
- Innovation Output: 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 |
|---|---|---|---|
| Evidence that AI helps women’s careers is thin and fragmentary: most studies audit bias or offer short-term skills support, while few measure long-term retention or advancement or report robust governance and accountability practices.openalex | Yasmina El Fassi orcid |
2026-06-04 | 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.