A very happy New Year to you all! I hope you have had a restful break and are looking forward to what the New Year has in store for us.  2016 was quite a year.  Who could have anticipated us leaving the EU, Trump coming to power in the US and the word ‘post-truth’ being selected as the Oxford Dictionary’s ‘Word of the Year’.  Thomas Carlyle, a Scottish philosopher, has a wonderful quote: “I do not believe in the collective wisdom of individual ignorance”.  When I reflect on the events of June and November in 2016, I think this quote sums it up beautifully.  Here’s to a better 2017.

Now, in this first blog of 2017, I had planned to write about the upcoming campus visits where I will be discussing our new R&E Strategic Plan, but I have decided to write about these after the visits are complete. So, instead, I am going to write about an interesting article that an old friend brought to my attention.  The article is entitled ‘Toward the Discovery of Citation Cartels in Citation Networks’ and can be accessed in full at http://journal.frontiersin.org/article/10.3389/fphy.2016.00049/full

In many disciplines, the number of citations that an article receives is considered a key measure of academic impact and quality. There are many reasons why an article might be cited.  Sometimes it is because it is the first/only study in the field, sometimes because it gives a good overview of the field or that it is of very high quality and describes the best available evidence in the field.  My most cited journal article (nearly 600 citations) was published in 2002 and is the first and, to date, the only article on a population-based study demonstrating the rise of allergy to peanuts.  I was lucky enough to follow two whole population birth cohorts, born 10 years apart, assessing the incidence of peanut allergy.  So, ever since the publication of this article, most studies in the area cite it.  I am very proud of it but it is, by no means, my best article.

The article I recommend you to read talks about the rising problem of citation cartels. These are defined as groups of authors that cite each other disproportionality more than they do other groups of authors that work in the same area, and which, therefore, artificially increases their citation rates.  The authors have come up with a model that can identify citation cartels by using semantic web tools. They state that their purpose is not to prevent this phenomenon, or to discredit authors that could be accidentally caught in the citation cartel, but to show that citation cartels exist, and that all responsible for publishing papers, Editors and Reviewers need to be aware of this.

I wonder if REF panels who consider citations will also take account of this!