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:
134160158
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- J. Fourie (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
Claim outcomes
- Market Structure: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| AI makes polished papers cheaper but trustworthy certification scarcer: as AI lowers the cost of producing well-presented manuscripts faster than it lowers the cost of judging scientific merit, submissions swell and the willingness to pay for credible review rises—allowing dominant certifiers to extract rents while constrained review capacity dilutes overall certification quality.openalex | J. Fourie provider id |
2026-07-15 | 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.