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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5126150661
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Hong Fai Choi (openalex, provider refresh)
- Hong Fai Choi (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Developer Productivity: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Research Productivity: 1 paper
- Task Allocation: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Scale AI demonstrates that labeling is not mere grunt work but a strategic engine: targeted, curated annotation and a platform-plus-services model turn messy client data into reusable assets that accelerate model development and capture value. Rather than raw volume, value comes from focused curation, failure discovery, and human feedback embedded in operational workflows.openalex | Hong Fai Choi provider id |
2026-02-17 | 0 |
Citation observation summary
OpenAlex 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.