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Bibliometrics in Practice
evaluating REF
Alan Dix
Talis and University of Birmingham
http://alandix.com/ref2014/
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
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
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?
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
Results – scary
apparent emergent bias – 5-10 fold!
sub-areas:
theory vs. applied
instututions:
Russell Group vs pre vs post 1992
+ gender impact

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Bibliometrics in Practice Evaluating REF Outputs

  • 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