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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5143903809
ORCID evidence
Observed aliases (2)
- Maria Luz Madariaga (openalex, provider refresh)
- Maria Luz Madariaga (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 1 paper
- Regulatory Compliance: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Attention-harvesting designs and recursive training on AI-generated content are unpriced externalities that may pose material financial risks to platform firms; the author proposes DAESG, a three-layer disclosure, standards, and capital-allocation framework to integrate these risks into mainstream financial governance.openalex | Maria Luz Madariaga provider id |
2026-07-29 | 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.