Paper on scientometrics
loet at LEYDESDORFF.NET
Mon Jul 29 01:13:21 EDT 2013
"Understanding," indeed, is always a first goal. When studying complex
systems, however, one risks to focus on the specificities in each case and
thus to specify variation. In a next step, the understanding can be used to
specify selection mechanisms that can be tested on other case materials or
against the whole database after upscaling.
For example, is concurrency of competing research programs a necessary
condition? Does a paradigm change lead to auto-catalytic growth that
overshadows other research programs - let's say after ten years? Or does it
more often lead to differentiation within specialties?
Perhaps, I should not have used the word "prediction" in this more technical
sense of statistical testing. Let's say: specification of an expectation. It
seems to me that lots of contributions to this discussion went in this
Selection is deterministic (unlike variation) and can therefore be tested.
Preferential attachment, for example, can be considered as a possible
Professor, University of Amsterdam
Amsterdam School of Communications Research (ASCoR)
Kloveniersburgwal 48, 1012 CX Amsterdam
<mailto:loet at leydesdorff.net> loet at leydesdorff.net ;
Honorary Professor, SPRU, <http://www.sussex.ac.uk/spru/> University of
Sussex; Visiting Professor, ISTIC,
From: ASIS&T Special Interest Group on Metrics
[mailto:SIGMETRICS at LISTSERV.UTK.EDU] On Behalf Of David Wojick
Sent: Sunday, July 28, 2013 8:29 PM
To: SIGMETRICS at LISTSERV.UTK.EDU
Subject: Re: [SIGMETRICS] Paper on scientometrics
Yes we often know a revolution when we see one, but that is not the same as
having an operational definition that lets us individuate them. We cannot
say for example how many revolutions occurred in discipline d during period
t. It is very hard to do meaningful empirical analyses of things we cannot
even count. Thus I think talk of prediction assumes a level of understanding
that we do not have. Understand is the goal in my view.
At 01:15 PM 7/28/2013, you wrote:
Dear David and colleagues,
One basic problem is that we do not have an agreed upon operational
definition of revolution. So if we are measuring different things under the
same name we may get differing results that do not actually disagree.
Although we don't have such a definition, it is not so difficult to point ex
post to instances that have provided breakthroughs and led to the
development of new specialties. For example, "oncogene" in 1988,
"interference RNA" in 1998; super-conductivity in 1987(?) at higher
It seems to me that there are two main questions that should not be
1. is it possible to predict such breakthroughs in terms of a specific set
of conditions? The notion of a void (as Chaomei named it) seems relevant
here: structural holes; synergies among redundant research programs, etc.
2. ex post: early warning indicators, upscaling conditions, etc. For
example, in the case of RNA-interference we hypothesized that first
preferential attachment is with the initial inventors, but then the system
globalizes and on preferentially attaches with world centers of excellence
(in Boston, London or Seoul). (Leydesdorff & Rafols, 2011).
In my opinion, the problem is that one can study these cases, derive
hypotheses, but then during the upscaling one fails to develop predictors
from them. For example, we found an entropy measure for new developments in
(Leydesdorff et al., 1994), but it did not work for the prediction at the
level of the file of aggregated journal-journal citations. Ron Kostoff's
tomography was another idea that eventually did not lead us to the
prediction of emerging fields (Leydesdorff, 2002).
I mean to say that if one finds for example, that an important new
development leads to a new citation structure, is it then also possible to
scan the database for such structures and in order to find new developments?
. Loet Leydesdorff, Susan E. Cozzens, and Peter Van den Besselaar,
Tracking Areas of Strategic Importance using Scientometric Journal Mappings,
Research Policy 23 (1994) 217-229.
. Loet Leydesdorff, Indicators of Structural Change in the Dynamics
of Science: Entropy Statistics of the
<http://www.leydesdorff.net/jcr/index.htm> SCI Journal Citation Reports,
Scientometrics 53(1) (2002) 131-159.
. Loet Leydesdorff & Ismael Rafols, How do emerging technologies
conquer the world? An exploration of patterns of diffusion and network
formation <http://www.leydesdorff.net/et/index.htm> , Journal of the
American Society for Information Science and Technology 62(5) (2011)
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