Following my post on citations in academic journals, I wanted to go one step further in the understanding of the dynamic of citations. So here, the dataset looks like that: for each article, we have the name of the journal, the year of publication (also the title of the article, but here we do not use it, as well as the authors), and more interesting, the number of citations in journals (any kind of academic journal) published in 1996, 1997, …, 2011. Of course, articles published in 1999 might have their first citation only starting in 1999.

base[1000:1002,] Publication.Year 7188 1999 7191 1999 7195 1999 Document.Title 7188 Sequential inspection 7191 On equitable resource approach 7195 Method for strategic Authors ISSN Journal.Title 7188 Yao D.D., Zheng S. 0030364X Operations Research 7191 Luss H. 0030364X Operations Research 7195 Seshadri S., Khanna A., Harche F., Wyle R. 0030364X Operations Research Volume Issue X139 DEV1996 DEV1997 DEV1998 DEV1999 DEV2000 DEV2001 DEV2002 7188 47 3 0 0 0 0 0 1 0 2 7191 47 3 0 0 0 0 0 0 2 0 7195 47 3 0 0 0 0 0 0 0 0 DEV2003 DEV2004 DEV2005 DEV2006 DEV2007 DEV2008 DEV2009 DEV2010 DEV2011 7188 0 0 0 1 0 0 0 0 0 7191 3 4 1 4 4 8 4 6 1 7195 0 1 2 2 1 0 1 0 0 X130655 X0 X130794 7188 4 0 4 7191 37 0 37 7195 7 0 7

The first step is to aggregate data, not to look at each article, but to look at all paper published in 1999 (say). And then, we look at the number in citations the year of publication, the year after, two years after, etc. It will appear in a triangle since if we look at articles published in 2010, there is only on possible year for citations (2010, since I removed 2011).

VOL=rev(unique(base$Volume)) VOL=VOL[is.na(VOL)==FALSE] TRIANGLE=matrix(NA,16,16) for(v in VOL){ k=k+1 sb=base[base$Volume==v,9:24] sb=sb[is.na(sb[,1])==FALSE,] TRIANGLE[k,1:(17-k)]=apply(sb,2,sum)[k:16]}

Then, a standard idea (at least in insurance business, for claims payment development) is to consider that data are Poisson distributed, and the number of citations should depend on the year of publication of the article (a row effect) and the development (how many years after are we looking at, i.e. a column effect). More formally, let denote the number of citations of articles published year during year (or after years). And we assume that

TRIANGLE=TRIANGLE[-16,] TRIANGLE=TRIANGLE[,-16] Y=as.vector(TRIANGLE) YEAR=rep(1996:2010,15) DEV =rep(1:15,each=15) baseT=data.frame(Y,YEAR,DEV) reg=glm(Y~as.factor(YEAR)+as.factor(DEV), data=baseT,family=poisson)

Since those are *incremental *values, in order to look at the paper of distribution, we need to sum them on a line. Thus, we can plot

(because we used factors, the first component has been replaced by the constant in the regression) or a normalized version to compare among journals. For instance, we would like to get 100 citations over 15 years.

DYN=exp(c(reg$coefficients[1],reg$coefficients[1]+ reg$coefficients[16:29])) DYNN=cumsum(DYN)/sum(DYN) plot(0:15,DYNN)

And this is what we get, for several academic journals,

The pattern is rather different. For instance, in *Health Economics*, citations is a quick process: more than 40% of citations obtained over 15 years, were obtained during the first 4 years. On the other hand, in the *Journal of Finance*, it is much smaller: less than 15% of the citations were obtained during the first 4 years (on average). So it means that comparing citation based index (namely *g* or *h*) is a difficult exercise, especially with you researchers in different areas. The same *g*or *h *index for young researcher, publishing either in *Stochastic Processes and their Applications *or *Annals of Statistics*, means that after 3 years, it can be 50% higher.

Now it is possible to look more into details, with below JRSS-B (on applied statistics). Note that here, citations come extremely slowly… to it might not be a good “strategy” (assuming that a researcher’s target is simply to get – quickly – a high citation index) for a young researcher to publish in JRSS-B

On the other hand, *Biometrika* is much faster (both are on applied statistics, but we’ve seen here that they were not in the same cluster)

We can also observe that *Annals of *Probability

and* Stochastic Processes and their Applications*

have (almost) similar patterns (SPA might be a bit faster). Anyway, I have been surprised to see that in theoretical journals citations are extremely fast. Especially if we compare with the *Journal of Finance* for instance

where I though citations were extremely fast. But I might have a non-correct interpretation: it might simply mean that in the *Journal of Finance* it is common to cite old papers (published 10 or 15 years ago), maybe more common that in stochastic processes…

Anyway, all suggestions about the interpretation are welcomed !

This is a nice interpretation. Especially when considering the Read Papers that are unique to Series B (and the RSS): those discussion papers have a very lasting impact on the field like the most famous EM paper of 1978 that is still quoted in about every paper on EM and its avatars.

Just correcting the injustice done to JRSS Series B and Biometrika: those journals are not applied at all, they are about statistical methodology. As an aside, JRSS Series B has the highest [two year and five year] impact factor in the field, close to three times higher than Biometrika, so suggesting a young researcher to submit to Biometrika rather than Series B may not be so helpful in the end! Of course, you may take this with a pinch of salt when coming from a former editor of Series B…

ANSWER: sorry Christian for the mistake (or for being imprecise)… what I meant by “applied” is that they deal with methodology and modeling issues… The term was probably not appropriate. Sorry….And actually, I wonder if the interpretation I give at the end is not better: in JRSS-B, it s not that there is a long delay before getting cited, but maybe simply that it is common to refer to some papers published 10 or 15 years ago in that journal. I remember that on my talks on extremes, I keep referring to Ledford and Tawn (1996 in Biometrika, 1997 in JRSS-B) since what they propose is a very interesting way of looking at extremes… I guess that a slow increase might interpreted as a sign of longevity of those articles, dealing with methodology…

Initially, I was willing to compare mathematical journal (that – from what I heard – have a slow citation process) and medicine journals (where it seams that it is not common to refer to old papers). Unfortunately, the Scopus access ended yesterday… so my study was somehow aborted.