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Background Peer review-based funding evaluation systems are under increasing pressure. To help alleviate this, innovative approaches like Distributed Peer Review (DPR) are being explored.
This study examines whether similarity between applicants and evaluators influences grant-review outcomes. The findings contribute to ongoing debates about fairness, bias, and the design of competitive research-funding systems.
If reviewers can't reliably tell the difference between top grant proposals, why should they pretend they can? A new wave of funders is turning to lotteries to break the tie.
Analyzing evaluation dynamics across the editorial review and peer review stages, this study finds strong selective effects associated with higher institutional prestige, larger team size, and certain topics and countries.
Large language models are beginning to enter peer review. Yet this use creates a security problem: the manuscript being judged can also contain hidden instructions that shape the model's judgment.
Emerging challenges are exposing limits to a system that was not designed to withstand. Peer review is at an inflection point.
The role of AI in peer review should be acknowledged in order to ensure practical guidance and policies that help scholars respond with consistency and confidence.
Generative AI is unsettling longstanding conventions of authorship, ownership and credit in academia and across cultural production. This has prompted responses that seek to reassert the role of the intelligent, creative human individual.
This article describes a course module that introduces MSc students at Utrecht University in the Netherlands to this part of the publication process.
If we were designing peer review from scratch for a world where powerful LLMs exist, what would we actually need humans for, and what could we comfortably automate?
Re-examining a 1997 paper claiming women academics need to work 2.4 times harder than their male counterparts, Ulf Sandström suggests what policymakers can learn from overreliance on single studies.
A randomized controlled study demonstrates that large language model-generated feedback can make reviews more informative while enhancing reviewer-author engagement. Preprint available: https://arxiv.org/abs/2504.09737
A study of millions of life science papers revealed that manuscripts with women in key authorship roles spent longer between submission and acceptance.
The groundbreaking inquiry follows a complaint from a Polish company that the use of AI to evaluate its EIC bid was unfair.
Generative AI is already being used by some universities to assess the quality of their research - and it could be scaled up to help all higher education institutions save huge amounts of time and money.
