PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality

2026-04-27

AI Summary

Peer review is experiencing exponential growth in volume, yet its quality remains highly variable, necessitating systematic, interpretable, and scalable evaluation tools. This work proposes PeeriScope—the first modular platform that integrates structured features, rubric-based large language model assessments, and supervised learning predictions to enable multidimensional, explainable quantification of review quality. PeeriScope supports diverse use cases including self-assessment by reviewers, editorial screening, and large-scale audit studies. Designed for real-world deployment and research extensibility, the platform offers an open API and a web interface. The project is open-sourced and accompanied by an online demo, aiming to foster continuous innovation and practical adoption of robust peer review evaluation methodologies.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• peer review quality
• scholarly publishing
• review evaluation
• quality assessment

Innovation

• peer review quality
• large language models
• modular framework
• structured evaluation
• extensible API