At 16.12.2005, Andrea Forte wrote:
I find it problematic to use number of edits and number
of authors
(quantitative date) as indicators of content quality. I'm willing to
believe that these are probably, in most cases, indicators of
improvement, but that's a huge assumption. To make this case, I think
some kind of qualitative analysis is necessary to demonstrate that the
article QUALITY improves by some set of standards and we'd expect that
these results will be correlated with number of authors/number of
edits. If anyone wants to collaborate on something like this, I might
have 15 or 20 minutes free in spring. ;-)
I agree, and to me it looks like Lih got it backwards: You would want
to show that some quantitative measures like number of edits
correlate positively with quality. As the paper stands, if someone
comes by and shows there is none or only a very weak correlation
between their quantitative indicators and actual quality of articles,
their paper becomes moot.
I would argue that you can assess article quality only by human
measure. Then you can go and show correlations with data like number
of edits, to later turn around and make predictions about quality of
papers based on these factors. But first you have to show the
strength of such correlation.
I think all attempts at reputation systems etc will fail if they are
purely algorithmical. Rather, I'd simply set up a voting system for
people to vote on the quality of an article they just read. That will
give you a reasonable measure of quality, against which you can run
experiments. (Why such voting works is a different topic.)
Dirk
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