Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

Q: What does this research mean for the field?

Automated quality ratings generated by generative models require systematic psychometric validation to be reliably used in peer review simulation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Олег Ивченко

doi:10.5281/zenodo.22546803

September 6, 2026Open Access

Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

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Key Points

To assess whether generative models can reliably simulate the peer review process and generate valid automated quality ratings for scientific manuscripts.
Simulated peer review assessments using generative artificial intelligence models.
Evaluated automated quality scoring against established academic review standards to assess validity.
Generative models demonstrate the capacity to synthesize evaluative feedback and assign automated ratings to academic manuscripts.
Validity analyses show structured alignment between automated quality ratings and standardized evaluation dimensions, though qualitative nuance varies.

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Implication

Evaluation demonstrates the validity of generative models for automated peer review scoring, highlighting artificial intelligence applications in academic evaluation.

Abstract

Research article: Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

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Олег Ивченко (2026) studied this question.

synapsesocial.com/papers/6a9fd78a58e84d0ff5b464b2 https://doi.org/10.5281/zenodo.22546803

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Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

Key Points

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Discussion

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Peer Review Simulation Using Generative Models: Assessing Validity of Automated Quality Ratings

Key Points

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Implication

Abstract

Discussion

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