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Development of a tool to accurately predict UK REF funding allocation

Al-Janabi, S., Lim, L.W. and Aquili, L. (2021) Development of a tool to accurately predict UK REF funding allocation. Scientometrics, 126 (9). pp. 8049-8062.

Link to Published Version: https://doi.org/10.1007/s11192-021-04030-w
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Abstract

Understanding the determinants of research funding allocation by funding bodies, such as the Research Excellence Framework (REF) in the UK, is vital to help institutions prepare for their research quality assessments. In these assessments, only publications ranked as 4* or 3* (but not 2* or less) would receive funding. Correlational studies have shown that the impact factor (IF) of a publication is associated with REF rankings. Yet, the precise IF boundaries leading to each rank are unknown; for example, would a publication with an IF of 5 be ranked 4* or less? Here, we provide a tool that predicts the rank of each submitted publication to (1) help researchers choose a publication outlet that would more likely lead to the submission of their research output(s) by faculty heads in the next REF assessment, thereby potentially improving their academic profile; and (2) help faculty heads decide which outputs to submit for assessment, thereby maximising their future REF scores and ultimately their research funding. Initially, we applied our tool to the REF (: Institutions Ranked by Subject (2014).

https://www.timeshighereducation.com/sites/default/files/Attachments/2014/12/17/g/o/l/sub-14-01.pdf.)) results for Neuroscience, Psychiatry, and Psychology, which predicted publications ranked 4* with 95% accuracy (IF ≥ 6.5), 3* with 98% accuracy (IF= 2.9–6.49), and 2* with 95% accuracy (IF= 1.3–2.89); thus indicating that researchers wishing to increase their chances of a 4* rating for the aforementioned Unit of Assessment should submit to journals with IFs of at least 6.5. We then generalised these findings to another REF unit of assessment: Biological Sciences to further demonstrate the predictive capacity of our tool.

Item Type: Journal Article
Publisher: Springer Verlag
URI: http://researchrepository.murdoch.edu.au/id/eprint/64096
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