Unfortunately true. This seems to be a pop-sci cover of some data mining and semantic clustering algorithm for scientific publications. The author claims the groupings may be better than citations (less political, for instance) in showing how ideas impact a given research community.
"It has proven difficult to provide a definitive account of scientific method that can decisively serve to distinguish science from non-science. Thus there are legitimate arguments about exactly where the borders are, which is known as the problem of demarcation." http://en.wikipedia.org/wiki/Science#Philosophy_of_science
Science is not currently a iron-cast term, irrespective of pithy aphorism.
Classifying science sounds like a boring application of this algorithm. I'd be more interested to see it used for spontaneously identifying groups of related publications any kind, such as blogs.
Blogs are a simple case because the default is to refer to each other through inline hyperlinks.
I've also seen linguistic data mining studies done that suggest that blogs which commonly agree with each other, commonly agree with each other. For purposes of politeness, I acted astonished at this revelation.
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[ 2.7 ms ] story [ 37.1 ms ] thread"It has proven difficult to provide a definitive account of scientific method that can decisively serve to distinguish science from non-science. Thus there are legitimate arguments about exactly where the borders are, which is known as the problem of demarcation." http://en.wikipedia.org/wiki/Science#Philosophy_of_science
Science is not currently a iron-cast term, irrespective of pithy aphorism.
There are others.
I've also seen linguistic data mining studies done that suggest that blogs which commonly agree with each other, commonly agree with each other. For purposes of politeness, I acted astonished at this revelation.
Here is video by him where he explains the basic model as well timeline-related hacks the article talks about - http://video.google.com/videoplay?docid=3077213787166426672#
You can very easily play with LDA yourself with this toolkit - http://mallet.cs.umass.edu/
I guess we could get the computer to categorise it ...
http://projecteuclid.org/euclid.aoas/1183143727
This work uses the correlated topic model, which extends LDA to model correlations among the extracted topics.
Edit: and this one [pdf]:
http://www.icml2010.org/papers/384.pdf