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Funding bodies launch consultation on future research assessment exercise

Research Excellence Framework

The findings of three reports initiated as part of the Future Research Assessment Programme (FRAP) have been published today.

Together the reports caution against moving to a fully metricised system for the next UK research assessment exercise. The reports do suggest ways in which metrics, AI or machine learning might be used to support or inform low-risk areas of the exercise, from allocating outputs to enabling post-assessment analysis. The four UK higher education funding bodies, who jointly oversee the Future Research Assessment Programme (FRAP), commissioned the reports as part of their broader evidence gathering process, including sector-wide consultation to inform decisions on the future of research assessment in the UK.

The three reports are:

The Metric Tide Revisited

A review of the role of metrics in research management and assessment, led by Professor Stephen Curry, Dr Elizabeth Gadd and Professor James Wilsdon. The report plots the development of the responsible research assessment agenda since the 2015 Metric Tide report. It revisits the potential use of indicators in any future REF exercise, proposing an increased use of ‘data for good’, and considers opportunities to further support the roll-out of responsible research assessment practice across the UK HE sector.

Responsible Use of Technology in Research Assessment

In early 2022, the four UK higher education funding bodies commissioned the Statistical Cybermetrics and Research Evaluation Group at the University of Wolverhampton. They were asked to investigate possible approaches to using technologies such as artificial intelligence and machine learning to support or streamline research assessment in future exercises. Overall, the study finds that such technologies could only be employed in future exercises in relatively low-risk areas of the assessment, such as allocation of research outputs to reviewers ahead of assessment, or for analytical purposes.

REF outputs analysis: Maximising the use of REF data

This report investigates how insights generated by the REF (or by future research assessment exercises) can be maximised to better understand the health of UK research. A series of analytical ‘experiments’ explore the feasibility of more granular analysis of research assessment data to gain insight into disciplinary and interdisciplinary strengths. The report finds that future exercises could incorporate some automation in post-assessment data analysis, but that such approaches should be complemented by peer review to validate any conclusions.

The reports are available online.