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Visualization and Analysis of Frames in Collections of Messages:
Content Analysis and the Measurement of Meaning
<at http://arxiv.org/ftp/arxiv/papers/1112/1112.6286.pdf >
Esther Vlieger & Loet Leydesdorff
A step-to-step introduction is provided on how to generate a semantic map
from a collection of messages (full texts, paragraphs or statements) using
freely available software and/or SPSS for the relevant statistics and the
visualization. The techniques are discussed in the various theoretical
contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii)
sociocybernetics and social systems theory (e.g., the communication of
meaning), and (iii) communication studies (e.g., framing and
agenda-setting). We distinguish between the communication of information in
the network space (social network analysis) and the communication of meaning
in the vector space. The vector space can be considered a generated as an
architecture by the network of relations in the network space; words are
then not only related, but also positioned. These positions are expected
rather than observed and therefore one can communicate meaning. Knowledge
can be generated when these meanings can recursively be communicated and
therefore also further codified.
Forthcoming in: Manuel Mora, Ovsei Gelman, Annette Steenkamp, and Maresh S.
Raisinghani (Eds.), Research Methodologies, Innovations and Philosophies in
Systems Engineering and Information Systems, Hershey PA: Information Science
Reference, 2012, pp. 322-340, doi: 10.4018/978-1-4666-0179-6.ch16.
Percentile Ranks and the Integrated Impact Indicator (I3)
< at http://arxiv.org/ftp/arxiv/papers/1112/1112.6281.pdf >
Loet Leydesdorff & Lutz Bornmann
We tested Rousseau's (in press) recent proposal to define percentile classes
in the case of the Integrated Impact Indicator (I3) so that the largest
number in a set always belongs to the highest (100th) percentile rank class.
In the case a set of nine uncited papers and one with citation, however, the
uncited papers would all be placed in the 90th percentile rank. A
lowly-cited document set would thus be advantaged when compared with a
highly-cited one. Notwithstanding our reservations, we extended the program
for computing I3 in Web-of-Science data (at this http URL) with this option;
the quantiles without a correction are now the default. As Rousseau
mentions, excellence indicators (e.g., the top-10%) can be considered as
special cases of I3: only two percentile rank classes are distinguished for
the evaluation. Both excellence and impact indicators can be tested
statistically using the z-test for independent proportions.
A shorter version of this paper is forthcoming as a Letter to the Editor of
the Journal of the American Society for Information Science and Technology
** apologies for cross-postings
Amsterdam School of Communications Research (ASCoR)
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