Toward an alternative bibliometric
Impact factor isn’t great. A bibliometric based on entropy reduction may be promising.
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Impact factor
There are a variety of citation-based bibliometrics. The current dominant metric is impact factor. It is highly influential, factoring into decisions on promotion, hiring, tenure, grants and departmental funding (Editors 2006) (Agrawal 2005) (Moustafa 2014). Editors preferentially publish review articles, and push authors to self-cite in pursuit of increased impact factor (Editors 2006) (Agrawal 2005) (Wilhite and Fong 2012). It may be responsible for editorial bias against replications (Neuliep and Crandall 1990) (Brembs, Button, and Munafò 2013). Consequently, academics take impact factor into account throughout the planning, execution and reporting of a study (Editors 2006).
This is Campbell’s law in action. Because average citation count isn’t what we actually value, when it becomes the metric by which decisions are made, it distorts academic research. In the rest of this post, I propose a bibliometric that measures the entropy reduction of the research graph.
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