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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2232283894
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Atharva Naik (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Other: 1 paper
- Inequality: 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 |
|---|---|---|---|
| Large language models replicate South Asian caste hierarchies in matchmaking: same-caste profiles score up to 25% higher, and inter-caste pairings are ranked according to traditional status across GPT, Gemini, Llama, Qwen and BharatGPT—raising risks that AI-mediated matchmaking could reinforce historical exclusion.openalex | Atharva Naik provider id |
2026-03-31 | 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.