A Futuristic View of Academic Peer Review

Peer review is at the heart of research, now and in the future. It's the gold standard for ensuring the quality and integrity of published research and has been for over 350 years. But problems within the system do exist. Peer review is often accused of being slow, inefficient, biased and open to abuse. That needs to change and this is the key question:

How can peer review be improved for future generations of academics?

Several recommendations came out of this. Transparency. Recognition. Training. Diversity. They're all in there - and we're proud to say Peereviewers is working hard on all fronts. There's no one approach to improving peer review and it won't happen overnight. But there are simple steps that can bring balance back to the system.

Here are the report's key recommendations for the research community as we head towards 2030:

  • Find and invent new ways of identifying, verifying and inviting peer reviewers, focusing on closely matching expertise with the research being reviewed to increase uptake. Artificial intelligence could be a valuable tool in this.

  • Encourage more diversity in the reviewer pool (including early career researchers, researchers from different regions, and women). Publishers in particular could raise awareness and investigate new ways of sourcing female peer reviewers.

  • Experiment with different and new models of peer review, particularly those that increase transparency.

  • Invest in reviewer training programs to make sure that the next generation of reviewers are equipped to provide valuable feedback within recognized guidelines.

  • Work towards cross-publisher solutions that improve efficiency and benefit all stakeholders. Portable peer review has not taken off at any scale, but could make the publishing process more efficient for all involved.

  • That funders, institutions and publishers must work together to identify ways to recognize reviewers and acknowledge their work.

  • Use technology to support and enhance the peer review process, including finding automated ways to identify inconsistencies that are difficult for reviewers to spot.