Bibliometrics in Practice - evaluating REF
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The document discusses the evaluation of bibliometrics in relation to the REF 2014 process, focusing on citation analysis and the performance differences across sub-areas in computer science. Alan Dix, a senior researcher, highlights discrepancies in research outputs, suggesting potential biases in evaluation metrics, particularly regarding applied versus theoretical research. The findings indicate significant variations in performance and point to the need for a more nuanced understanding of bibliometric data in assessing research quality.
Bibliometrics in Practice - evaluating REF
- 1. Bibliometrics in Practice evaluating REF Alan Dix Talis and University of Birmingham http://alandix.com/ref2014/
- 2. Who – Alan Dix first love mathematics all of it! … including numerical models and statistics academic for 35 years human computer interaction + all sorts Talis senior researcher T&L software for HE (reading lists, etc.) REF 2014 panel member SP11 Computer Science and Informatics
- 3. What – REF public domain data (virtually) complete list of outputs: – excluding a few confidential ones – for each: name, doi, ACM topic area, Scopus citations (where used) Google scholar citations for each – gathered after REF (not used in assessment) UoA profiles sub-area profiles N.B. highlighted data SP11 only
- 4. Why? sub-area profiles – large differences applied and human-oriented areas low 4* various possible reasons: weak UK research long tail bias in process how to distingish?
- 5. How – bibliometrics citation analysis – Scopus & Google Scholar N.B. metrics to validate evaluation ≠ metrics for evaluation large numbers 500–1000 per sub area low 100s per institution
- 6. Results – scary apparent emergent bias – 5-10 fold! sub-areas: theory vs. applied instututions: Russell Group vs pre vs post 1992 + gender impact