File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Development of a tool to accurately predict UK REF funding allocation

TitleDevelopment of a tool to accurately predict UK REF funding allocation
Authors
KeywordsREF
Impact factor
Metrics
Funding
Issue Date2021
PublisherSpringer Verlag, co-published with Akademiai Kiado Rt. The Journal's web site is located at http://link.springer.com/journal/11192
Citation
Scientometrics, 2021, v. 126 n. 9, p. 8049-8062 How to Cite?
AbstractUnderstanding 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.
Persistent Identifierhttp://hdl.handle.net/10722/300855
ISSN
2023 Impact Factor: 3.5
2023 SCImago Journal Rankings: 1.079
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAl-Janabi, S-
dc.contributor.authorLim, LW-
dc.contributor.authorAquili, L-
dc.date.accessioned2021-07-06T03:11:09Z-
dc.date.available2021-07-06T03:11:09Z-
dc.date.issued2021-
dc.identifier.citationScientometrics, 2021, v. 126 n. 9, p. 8049-8062-
dc.identifier.issn0138-9130-
dc.identifier.urihttp://hdl.handle.net/10722/300855-
dc.description.abstractUnderstanding 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.-
dc.languageeng-
dc.publisherSpringer Verlag, co-published with Akademiai Kiado Rt. The Journal's web site is located at http://link.springer.com/journal/11192-
dc.relation.ispartofScientometrics-
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11192-021-04030-w-
dc.subjectREF-
dc.subjectImpact factor-
dc.subjectMetrics-
dc.subjectFunding-
dc.titleDevelopment of a tool to accurately predict UK REF funding allocation-
dc.typeArticle-
dc.identifier.emailLim, LW: limlw@hku.hk-
dc.identifier.authorityLim, LW=rp02088-
dc.description.naturepostprint-
dc.identifier.doi10.1007/s11192-021-04030-w-
dc.identifier.scopuseid_2-s2.0-85108583789-
dc.identifier.hkuros323222-
dc.identifier.volume126-
dc.identifier.issue9-
dc.identifier.spage8049-
dc.identifier.epage8062-
dc.identifier.isiWOS:000664849500021-
dc.publisher.placeHungary-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats