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
8Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
153518882
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- B. Harris (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Other: 1 paper
- Automation Exposure: 1 paper
- Employment: 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 are broadly getting better across text tasks rather than suddenly mastering narrow task clusters: measured success rose from roughly 50% in mid‑2024 to 65% by mid‑2025 and is projected to reach 80–95% for most text tasks by 2029. That suggests gradual, economy‑wide automation (‘rising tide’) rather than imminent, concentrated disruptions, although actual labor impacts will depend on organizational adoption and quality requirements.arxiv | B. Harris provider id |
2026-04-01 | 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.