Citation Statistics
arXiv:0910.3529 · doi:10.1214/09-STS285
Abstract
This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using ``simple and objective'' methods is increasingly prevalent today. The ``simple and objective'' methods are broadly interpreted as bibliometrics, that is, citation data and the statistics derived from them. There is a belief that citation statistics are inherently more accurate because they substitute simple numbers for complex judgments, and hence overcome the possible subjectivity of peer review. But this belief is unfounded.
This paper commented in: [arXiv:0910.3532], [arXiv:0910.3537], [arXiv:0910.3543], [arXiv:0910.3546]. Rejoinder in [arXiv:0910.3548]. Published in at http://dx.doi.org/10.1214/09-STS285 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (5)
- An index to quantify an individual's scientific research output
- An index to quantify an individual's scientific research valid across disciplines
- National Scientific Facilities and Their Science Impact on Non-Biomedical Research
- Generalized h-index for Disclosing Latent Facts in Citation Networks
- Universal Behavior of a Research Productivity Index
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