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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2222854159
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Matthew Akuzawa (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Inequality: 1 paper
Claim outcomes
- Consumer Welfare: 1 paper
- Inequality: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 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 nudge users toward textbook financial planning: simulated adherence to GPT-5.2 recommendations raises stock-market participation, increases savings buffers and produces age‑declining equity shares. But advice differs by user — men, prior AI users and the financially literate receive more equity-heavy and higher-saving guidance, with most of the gender gap driven by what people ask and a smaller portion by how the model responds.arxiv | Matthew Akuzawa provider id |
2026-08-03 | 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.