From Gatekeeping to Bottlenecks: The Peer Review Crisis in Academia

Dr. Akash Saxena

Dr. Akash Saxena is currently working as Professor and Head of the Department in School of Engineering & Technology, Central University of Haryana, India. Previously he was associated with Department of Computer Science (Artificial Intelligence) at Vellore Institute of Technology, University (Bhopal) Campus. As an academician, he has been associated with numerous institutions as Member of Academic Council, Doctoral Research Committees and Member, Board of Studies. He has presented his research work at various platforms to showcase his findings to the fellow researchers and providing a pathway to the future researchers. His intellectual work has been published in leading journals in the form of short communication/letters /articles/ research papers. Dr. Saxena is amongst the top 2% scientists of World in the field of Artificial Intelligence, this ranking is given by Stanford University and Elsevier jointly on the basis of quality publications and citation counts.

This system would be fast reliable and based on the expertise identification of the reviewer, punctuality of the reviewer in submission of the review and research score of the reviewer.

Research is an integral part of the higher education system. The paper publication in reputed journals is inevitable as it is an indicator of author reputation and often it is included as an important parameter of institution rankings. As a result of this, many institutions frame policies to encourage teachers. These policies include Article Processing Charge reimbursement, incentive based on the quality of publication, rewards and many more.

With this, there is tsunami of journal papers submission in recent years. With this massive rate of submission, publication platforms are experiencing many problems. The major problem is to find potential reviewers, editors for submitted papers and timely decisions for each submission. Editors are facing problems due to interdisciplinary nature of the work as the assignment of the reviewer should be done as per the expertise in multidomain knowledge. Identification of such reviewers who come from this multidisciplinary background is a daunting task.

Reviewer Fatigue and Shortage

Peer review process demands careful reading and understanding of the research work presented in the manuscript along with the deep understanding of the experimentation conducted. Often submitted manuscript lacks in the data reproduction facility due to various reasons. With this limitation, the work can’t be authenticated and moreover reviewers also do multiple academic duties in their academic institutions. Hence, the time required for judging the manuscript on the basis of research quality is scant.

If we do simple mathematical analysis and consider the rate of submission as exponential trend over the years, the reviewer availability is represented as a linear graph. This analysis shows that in coming years, the gap between both trends will increase. This indicates that in coming years, availability of the reviewers for peer review is limited.

Another reason of unavailability is due to lack of motivation and incentives associated with the peer review tasks. Hence it is considered as invisible labour. 

Impact of AI and Automation

In the era of artificial intelligence, papers are written with the help of AI. This framework suggests you the outline of the paper and provide the research review. Recently it has been observed that some of the papers are retracted due to unethical use of AI in the writing. Sometime, reviewer also use Large Language Models for writing the reviews of a technical manuscript and they upload manuscripts on such common platforms. The impact of this is hazardous as Ai learns new experimentation-based case and saved it for other queries and it also provides generic review for the work which lacks in the quality.

Delay in Publication Cycle

Sometimes journals set long review timelines and recommendation systems are not having the feature for selecting competent reviewer which obeys the deadlines also. This impact as, longer review timelines of the article and that also affects the motivation of potential authors. Though delay of publication is also due to denial of previous reviewers who reviewed the manuscript.

Bias and Subjectivity in Peer Review

An objective evaluation is expected for accessing the scientific quality of the manuscript. However, still the editors and reviewers are human and their decision is influenced with cognitive bias, professional experience, institutional affiliation and personal beliefs. The bias and subjectivity are long-debated issue in peer review analysis. Further, the bias may be segregated as Institutional Bias (Prestige Bias), gender bias, geographic bias and confirmation bias. Refining all these biases from the peer reviews make it a transparent and clean process. This burning problem can be accessed with the integration of optimization model combining the review deadline and availability of the reviewers. A framework for this can be integrated in the web-based platform. Further, to ensure the research integrity advanced cyber security-based protocols can be implemented. The engine may detect duplicate submissions and report to the editors. Last but not the least to have AI based reviewer recommendation system. This system would be fast reliable and based on the expertise identification of the reviewer, punctuality of the reviewer in submission of the review and research score of the reviewer.