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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2405889002
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ibrahim Denis Fofanah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Employment: 1 paper
- Hiring: 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 |
|---|---|---|---|
| Keyword-based automated screening inflates search frictions by misreading candidate skills, while semantic, vector-based matching markedly raises candidate recall and matching efficiency in simulations; adopting interoperable, verified candidate-side signals could cut matching costs but requires real-world validation.openalex | Ibrahim Denis Fofanah provider id |
2026-01-20 | 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.