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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2421614750
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Red Bermejo (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Hiring: 1 paper
- Employment: 1 paper
- Adoption Rate: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| There are roughly a thousand technically-competent ML researchers working in ML consulting firms worldwide — about twice the number of alumni from the MATS program — but only a small fraction of consultancies clear a hands-on research-and-engineering work trial, and no AI model passed that trial by late 2025.arxiv | Red Bermejo provider id |
2026-02-10 | 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.