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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2239932794
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Julia Schmidt (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Hiring: 1 paper
- Market Structure: 1 paper
- Other: 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 |
|---|---|---|---|
| Advertised demand for data skills clusters in the same occupations across the US, UK and Canada, but its industrial footprint varies — suggesting common skill complements to data assets alongside country-specific labor-demand structures.openalex | Julia Schmidt provider id |
2026-01-06 | 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.