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:
2326132425
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Julia De Miguel Velázquez (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Error Rate: 1 paper
- Worker Satisfaction: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Three-quarters of AI design mismatches trace back to developers’ focus on speed: 83% of workplace AI incidents arise when systems don’t match worker needs, and developers’ emphasis on efficiency explains 74% of task-level misalignments, especially in people-facing roles.arxiv | Julia De Miguel Velázquez provider id |
2026-05-20 | 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.