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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2409656023
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Gabrielle Teyssier-Roberge (semantic scholar, provider refresh)
- Gabrielle Teyssier-Roberge (semantic scholar, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Other: 1 paper
- Team Performance: 1 paper
- Training Effectiveness: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 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 |
|---|---|---|---|
| AI raises productivity only when it functions as a calibrated teammate and humans are trained to collaborate; without shared understanding, flexible communication and calibrated trust, firms risk errors and wasted investment. Cross‑training and co‑learning systems appear most promising, but heterogeneous, mostly small‑scale evidence limits precise, generalizable conclusions.semantic_scholar | Gabrielle Teyssier-Roberge provider id |
2026-08-03 | 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.