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:
2449293692
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Lige Gan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Employment: 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 |
|---|---|---|---|
| AI is better at doing than judging: occupations concentrated in execution tasks have seen slower employment growth since 2012, a pattern that predates the recent AI surge; AI-capability scores rise after 2022, but the paper documents timing and measurement rather than proving an AI-driven causal effect.openalex | Lige Gan provider id |
2026-07-23 | 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.