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:
48933958
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Avram (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Inequality: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Consumer Welfare: 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 |
|---|---|---|---|
| GeoAI sharpens urban decision-making—improving risk maps, informal settlement detection and targeted policy evaluation—yet its benefits concentrate where data, technical capacity and governance are strongest. Without investment in open data, transparency standards and local capacity, GeoAI risks reinforcing rather than reducing urban inequalities.openalex | S. Avram provider id |
2026-03-11 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.