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:
2481644594
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Мария Станиславовна Кузнецова (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Decision Quality: 1 paper
- Consumer Welfare: 1 paper
- Job Displacement: 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 |
|---|---|---|---|
| Churn scores mislead marketers: customers ranked highest by predicted risk are often not the ones most moved by retention offers, and switching to response-targeting raised churn reduction by up to 6.8 percentage points in field tests; firms must build experimental and causal-ML infrastructure and reorganize CXM to act on treatment-effect heterogeneity.openalex | Мария Станиславовна Кузнецова provider id |
2026-09-11 | 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.