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AI evidence extraction

After the Wow: Building Reliability in GenAI ‐Era Scholarly Publishing

Hengzhi Hu, Luying Zheng, Wei Lun Wong · Learned Publishing · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10
Relevance
1/4
Quality (LMQS)
I
Evidence
0
Citations
0.00
FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1002/leap.2081

Methodology & findings

Study design

Conceptual analysis and argumentative framework development based on critical examination of scholarly publishing practices in the GenAI era

Main result

The paper identifies that "the main challenge introduced by GenAI is not only text generation, but whether reliability can be built into publishing workflows." The authors argue that "Scholarly credibility depends on verification signals that travel across manuscripts, metadata, indexing and reuse," suggesting that reliability must be systematically embedded across multiple layers of the publishing ecosystem.

Reports effect sizes.

Research paradigm

Critical/Interpretivist

Author conclusions

The authors conclude that "Retraction checks and GenAI-use records should become embedded, structured, auditable and machine-readable," and that "Reliability routines require visible validation labour and assessment systems that reward transparency." This reflects a prescriptive vision for reforming publishing workflows.

Open questions raised

  • The authors identify the need for systematic integration of GenAI-use records and retraction checks into publishing workflows, as well as the development of assessment systems that incentivize transparency in scholarly publishing.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 93%

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