Paper on scientometrics

Chen,Chaomei cc345 at DREXEL.EDU
Sun Jul 28 09:16:56 EDT 2013

To address what may serve as an early sign for a conceptual revolution, a few lines of research are particularly relevant and share something in common: it seems to be worthwhile watching the currently void but potentially somewhere that could be filled up very rapidly (e.g. Don Swanson on disjoint publically available knowledge, Lee Fleming on patents, Loet+Ismael+Porter on interdisciplinarity, Klavans et al. on conformity, and our own work on structural variation). Of course, this may be just one of many potentially ways that may lead to a revolution.

An intuitive metaphor is that one way to empirically detect such signals is to look at new 'conceptual' bridges emerging and connecting previously isolated islands of thinking. If a revolutionary idea eventually takes off, then we'd expect to see a rapid increase of traffic on at least some of these bridges so much so that at the system-level the global landscape transforms its previous structure to something noticeably different.

I don't know how to post a picture to this list, but here is a link to a visualization that shows, retrospectively, after the Watts 1998 paper, which has since attracted so much attention to the study of complex networks such as small-world, scale-free networks, how previously separated islands are stitched together by subsequent publications that reinforce the structural change.

For details, see:
Chen, C. (2012) Predictive effects of structural variation on citation counts. Journal of the American Society for Information Science and Technology, 63(3), 431-449.

Chaomei Chen
From: ASIS&T Special Interest Group on Metrics [SIGMETRICS at LISTSERV.UTK.EDU] on behalf of David Wojick [dwojick at CRAIGELLACHIE.US]
Sent: Sunday, July 28, 2013 8:02 AM
Subject: Re: [SIGMETRICS] Paper on scientometrics

Dear Lutz,

Note the exponential growth potential of the issue tree. If the rate of branching is just three nodes per node then the tenth level already has about 60,000 nodes. This is how new ideas can take off so rapidly, when community attention turns to them. That is the normal science phase, when the new idea is being actively investigated. The revolution is just the top of the tree. There is a lot of confusion about this. The period of rapid growth is not the revolution, rather it is the normal product of the revolution. In many ways it is more interesting than the revolution. It is like the difference between a gold strike and a gold mine. Mining is interesting, and labor intensive.

Your search for empirical studies may be premature. One first needs a theoretical framework (says the theoretician).


On Jul 28, 2013, at 1:04 AM, "Bornmann, Lutz" <lutz.bornmann at GV.MPG.DE<mailto:lutz.bornmann at GV.MPG.DE>> wrote:

Thanks for the link to the tree model. Interesting! But I am searching for large-scale empirical studies.

This is an interesting question: in which time period is the productivity (in terms of publication numbers) higher: in normal science or during revolutions? If one looks back on the scientific progress in a discipline, the progress is normally described alongside big discoveries (revolutions). Periods of normal science are not so interesting here, although most of the papers in the discipline might have been published in these periods.


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Am 27.07.2013 um 22:23 schrieb "David Wojick" <<mailto:dwojick at CRAIGELLACHIE.US>dwojick at CRAIGELLACHIE.US<mailto:dwojick at CRAIGELLACHIE.US>>:

ml On the theoretical side my issue tree model of scientific progress helps explain the growth of subfields. See <>
<> .

But I am puzzled by your second paragraph. Most progress occurs during normal science for that is when many specific things get explained. Revolutions are not productive when they are occurring. The productivity comes during the subsequent normal science period.


At 03:41 PM 7/27/2013, you wrote:
Great comment, Andrea! Concerning altmetrics, these new metrics have been more and more examined. Most of the studies analyzed their correlations with citations. Because the correlation is far from perfect, it is not clear which aspects are really measured. I believe that "advanced" altmetrics (which will be developed) will be able to measure some kind of societal impact.

The later Kuhn described two possible ways of scientific progress in a field: the first way is a revolution; the second way is specialization by the creation of subfields.

Colleagues, are you aware of large-scale empirical studies which examined the development of the subfield structure in disciplines?


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Am 27.07.2013 um 14:54 schrieb "Andrea Scharnhorst" < <mailto:andrea.scharnhorst at DANS.KNAW.NL> andrea.scharnhorst at DANS.KNAW.NL<mailto:andrea.scharnhorst at DANS.KNAW.NL>>:

Dear all,

The raise of scientometrics has different roots: the societal need for monitoring expenses in time of a growing science system and the emergence of knowledge-based societies; and  the need for efficient information retrieval and knowledge discovery as a service for the sciences themselves, and here I echo contributions of others.

Having as object of study scholarship, it is only naturally that with changes in this very scholarship also the topics and methods of scientometrics change. There has been a longer debate if digital scholarship presents a revolution or not. (see also Wouters et al. Virtual Knowledge, MIT 2013)

What of these changes should be called a revolution, for sure depends on the point of reference. I always find Galison's approach to scientific revolution helpful. He argues that breaks and changes occur in theoretical threads as well as in empirical one and in methodological one; sometimes this occurs in parallel, sometimes with a time delay; sometimes in one specialty only – sometimes affecting a whole field. So, instead of looking at a singular event, one better can talk of an accumulation of different changes. Galison uses often geological, geomorphological metaphors to describe this. (see his book: Image and Logic). I think one cannot talk about a revolution with defining the boundaries of the system of reference first.

As an observer of scientometrics from the periphery or better as an occasional visitor, I found remarkable how in the past the scientometrics community embraced and integrated the visual turn (science maps) and the turn towards the authors. The latter was very visible at the last ISSI just a week ago. ( <> )

My impression is also that scientometrics managed to claim authority in the turn from "little bibliometrics" to "big bibliometrics" as Wolfgang Glaenzel called it in 2006, in a presentation I still find interesting to watch/read (see <> ). I'm not sure if Wolfgang would still support his statement from seven years ago that "bibliometrics evolved from a sub discipline of LIS to a evaluation and benchmarking tool". But, it seems that it is still the scientometrics community which discusses and defines indicators used broadly.

What concerns the digital revolution, and in particular the web, indeed scientometrics has incorporated altmetrics, taken up the challenge and made own original contributions (thanks to pioneers as Judit BarIlan, Mike Thelwall, Isidro Aguillo and many others). But, if I may say so, here scientometrics acted rather as a client, using the new data sources. Its behavior towards web-based information was and is very similar to the behavior towards the commercial bibliographic databases: namely to build indicators based on data export from them.

What I think is a challenge to be mastered in the upcoming years, is the semantic web and Linked Open Data. Here, I would like to back-up Clement's contribution, and actually reading his list and the thread as a whole triggered this now growing more lengthy comment ;-)

If the attempts of the semantic web community mature further, and if research information as a standard becomes available semantic referencable on the web, we talk about a profound change in the data source landscape for scientometrics. There is a possibility to eventually link between the 'old' input/expenditure statistics, human capital information and other institutional information and the traditional output – the scholarly communication – which for so many decades has dominated scientometrics, also just because of its availability in a standardized form. One example for this movement is VIVO, <><>. But, working in a research data archive I witness the raise of standards, API's, LOD in this area and it is obvious that the web of scholarly information is just before (in not in the middle) of another big change.

Semantic reasoning over research information in Linked Open Data (LOD) formats will enable services (including indicators) different from what we have now. It does not concern 'just another data base' or another social media one can harvest data from; it concerns a whole set of other techniques. Either scientometrics embraces this too, and learns to play on the "Klaviatur" (keyboard) of the semantic web, or the knowledge of the community might become obsolete.

Personally, I think the LOD and semantic web technologies are the future methodological innovation in scientometics, and I'm curious to hear comments on this. According to Lutz, this than would not count as a revolution, being 'just' another method. But, if it means that other communities could become the carrier for scientometric analysis, it might be a revolution – at least for the field of scientometics as we know it now.

Dr. Andrea Scharnhorst
Head of e-research at Data Archiving and Networked Services (DANS)
Scientific Coordinator of the Computational Humanities Programme, e-Humanities group
Chair of the COST Action KnowEscape
Royal Netherlands Academy of Arts and Sciences

From: Clement Levallois < <mailto:clement_levallois at YAHOO.FR> clement_levallois at YAHOO.FR<mailto:clement_levallois at YAHOO.FR>>
Date: Fri, 26 Jul 2013 16:41:46 +0200
Subject: Re: [SIGMETRICS] Paper on scientometrics


Difficult to say where we are going, but there is an expanding list of practices that are pushing for an evolution of scientometrics.

- open access

- open data (Figshare, etc.)

- semantic web / linked data

- science communication / science making on social media

- digital scholarship (see <>

- networks (can we neglect relations between the units under measurement?)

- the altac movement

- and altmetrics (drawing on all the previous)

I'd be curious, what else do you see as "disruptive" (sorry for the buzz word) today in scientometrics?

Best regards,


Clement Levallois, PhD
Erasmus University Rotterdam
The Netherlands

pro website<> / personal website<>

twitter and skype: @seinecle
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