Paper on scientometrics

[David Wojick]

Dear Fil,

Presumably the social interactions are driven by what the people are 
thinking, talking and writing about, which in the case of science is how 
the natural world works (including the human world). Social interactions 
are thus a dependent variable to thought and understanding. These 
interactions are issue driven so issues and their attention are the 
independent variables.

David Wojick

At 12:55 PM 7/29/2013, you wrote:
>Adminstrative info for SIGMETRICS (for example unsubscribe):
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>
>Dear Loet et al.,
>
>On Sun, Jul 28, 2013 at 2:36 AM, Loet Leydesdorff <loet at leydesdorff.net> 
>wrote:
> >
> > It seems to me that in your paper scientific developments are exogenous:
> > “and exogenous events, such as scientific discoveries.” You assume that
> > collaborations in social networks (e.g., coauthorships) are the drivers of
> > new developments. One could argue that this is the case in normal science
> > more than in periods of radical change.
>
>You are right that our contribution (in the paper I mentioned earlier:
>http://dx.doi.org/10.1038/srep01069) was more along the distinction
>between endogenous and exogenous change, than between normal and
>radical change --- the latter is an output rather than an input of our
>model. For example we observe some disciplines emerging and
>"exploding" in popularity, just as we find in the empirical data.
>
>Our point was to see how much one could predict or explain (in a
>quantitative sense) the empirical data about the evolution of
>disciplines (and their relationship to authors and papers) under the
>assumption that endogenous (social) interactions are the main (in our
>model, the only) drivers in the dynamics of science. The key
>contribution is the empirical validation of the model against data (in
>our case, three large-scale data sets), suggesting the model is quite
>successful and therefore the assumption is valid --- to the extent of
>the accuracy of our predictions.
>
>So, yes, one could definitely argue that exogenous changes exist (I
>believe it). But if one wants to argue that such changes are
>*necessary* to explain the evolution of science, one has to test such
>assumptions against empirical data, and show that they generate better
>quantitative predictions/explanations of the data, compared to a
>simpler model without exogenous events.
>
>Cheers,
>-Fil
>
>P.S. As a footnote, I find it exciting that we have people with
>diverse backgrounds contributing to this debate. I am a newcomer in
>this area; our group's background is a mix of physical, computing, and
>information sciences. We are particularly interested in quantitative
>models to be empirically validated against large scale data across
>disciplines, rather than against particular case studies or examples.
>But we're learning a lot from all the different contributions in this
>list. Thanks for the feedback!
>
>Filippo Menczer
>Professor of Informatics and Computer Science
>Director, Center for Complex Networks and Systems Research
>Indiana University, Bloomington
>http://cnets.indiana.edu/people/filippo-menczer
>
>
>On Mon, Jul 29, 2013 at 1:13 AM, Loet Leydesdorff <loet at leydesdorff.net> 
>wrote:
> > Adminstrative info for SIGMETRICS (for example unsubscribe):
> > http://web.utk.edu/~gwhitney/sigmetrics.html
> >
> > Dear David,
> >
> >
> >
> > “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
> > direction.
> >
> >
> >
> > Selection is deterministic (unlike variation) and can therefore be tested.
> > Preferential attachment, for example, can be considered as a possible
> > selection mechanism.
> >
> >
> >
> > Best,
> >
> > Loet
> >
> >
> >
> > ________________________________
> >
> > Loet Leydesdorff
> >
> > Professor, University of Amsterdam
> > Amsterdam School of Communications Research (ASCoR)
> >
> > Kloveniersburgwal 48, 1012 CX Amsterdam
> > loet at leydesdorff.net ; http://www.leydesdorff.net/
> > Honorary Professor, SPRU, University of Sussex; Visiting Professor, ISTIC,
> > Beijing;
> > http://scholar.google.com/citations?user=ych9gNYAAAAJ&hl=en
> >
> >
> >
> > 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
> >
> >
> >
> > Adminstrative info for SIGMETRICS (for example unsubscribe):
> > http://web.utk.edu/~gwhitney/sigmetrics.html
> >
> > Dear Loet,
> >
> > 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.
> >
> > David
> >
> > At 01:15 PM 7/28/2013, you wrote:
> >
> > Adminstrative info for SIGMETRICS (for example unsubscribe):
> > http://web.utk.edu/~gwhitney/sigmetrics.html
> > 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
> > temperatures, etc.
> >
> > It seems to me that there are two main questions that should not be
> > confused:
> >
> > 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?
> >
> > Best,
> > Loet
> >
> > References:
> > ·        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 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, Journal of the American Society for Information Science and
> > Technology 62(5) (2011) 846-860.
> >