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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2442807725
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ramya Pachatcharam (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Decision Quality: 1 paper
- Regulatory Compliance: 1 paper
- Adoption Rate: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Governance And Regulation: 1 paper
- Task Completion Time: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| An AI-enabled placement tool cut search time and tightened adherence to housing-placement rules in a London-borough pilot, raising officer satisfaction while keeping legal accountability intact; the modular cloud design promises replication across councils but evidence is limited to a single-site trial.arxiv | Ramya Pachatcharam provider id |
2026-06-15 | 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.