Open Access Metrics: Use REF2014 to Validate Metrics for REF2020 - Open Access Archivangelism
Steven Hill of HEFCE has posted “an overview of the work HEFCE are currently commissioning which they are hoping will build a robust evidence base for research assessment” in LSE Impact Blog 12(17) 2014 entitled Time for REFlection: HEFCE look ahead to provide rounded evaluation of the REF Let me add a suggestion, updated for REF2014, that I have made before (unheeded): Scientometric predictors of research performance need to be validated by showing that they have a high correlation with the external criterion they are trying to predict. The UK Research Excellence Framework (REF) -- together with the growing movement toward making the full-texts of research articles freely available on the web -- offer a unique opportunity to test and validate a wealth of old and new scientometric predictors, through multiple regression analysis: Publications, journal impact factors, citations, co-citations, citation chronometrics (age, growth, latency to peak, decay rate), hub/authority scores, h-index, prior funding, student counts, co-authorship scores, endogamy/exogamy, textual proximity, download/co-downloads and their chronometrics, tweets, tags, etc.) can all be tested and validated jointly, discipline by discipline, against their REF panel rankings in REF2014. The weights of each predictor can be calibrated to maximize the joint correlation with the rankings. Open Access Scientometrics will provide powerful new means of navigating, evaluating, predicting and analyzing the growing Open Access database, as well as powerful incentives for making it grow faster.
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