PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality
It addresses the need for systematic, interpretable tools to assess peer review quality in scholarly venues, but the contribution is incremental as it combines existing methods.
PeeriScope is a modular platform that integrates structured features, rubric-guided LLM assessments, and supervised prediction to evaluate peer review quality across multiple dimensions, providing a public interface and API for deployment and research.
The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools to assess review quality. We present PeeriScope, a modular platform that integrates structured features, rubric-guided large language model assessments, and supervised prediction to evaluate peer review quality along multiple dimensions. Designed for openness and integration, PeeriScope provides both a public interface and a documented API, supporting practical deployment and research extensibility. The demonstration illustrates its use for reviewer self-assessment, editorial triage, and large-scale auditing, and it enables the continued development of quality evaluation methods within scientific peer review. PeeriScope is available both as a live demo at https://app.reviewer.ly/app/peeriscope and via API services at https://github.com/Reviewerly-Inc/Peeriscope.