Steve Bennett wrote:
On 4/10/06, Bryan Derksen bryan.derksen@shaw.ca wrote:
Deterioration might not be so bad. In evolutionary optimization (for example via genetic algorithms) a concept that's often mentioned is the "fitness landscape", a graph showing the relative fitness of all possible solutions to a particular problem. The landscape for most
I'm familiar with this problem, but do you know of any detailed (if not particularly formal) analysis to see if it applies to Wikipedia, or even wiki editing in general? Not infrequently you hear people say "this article is a mess, nothing short of rewriting is required here". But I'm not convinced. Is it not possible, given an article in state T0, where state Tp is perfection, find small, realistic changes T1, T2 etc?
More often than not, people will add stuff to an article, and some time later, another editor will come along and clean it up. Then some more stuff will be added, and some time later, it will get cleaned up again. Very rarely do you see an article /only/ get better (one example I *can* think of is [[Jordanhill railway station]] http://en.wikipedia.org/wiki/Jordanhill_railway_station), but that was an extraordinary case.
reach. For example, new material might be added covering some aspect of the article's topic that wasn't covered previously, but the new material has bad grammar and sparse citations and is "bad" enough to knock the article off of Featured status. The best approach in this case is not to just revert to the old version, but rather clean up the new material to result in an even better article than it was before.
In this particular instance, it seems that you could add *some* of the new material in a small enough block that the lack of citations or poor copyediting does not knock the article off its FA perch. Then, when that material has been brought up to scratch, add the next batch. Wholesale additions of poor quality material are generally quite destabilising for an article, and tend to piss off existing editors, who wonder when it will all stop...
I agree. Although the evolutionary optimization analogy works some of the time (generally up until the point the article is featured), entropy starts to take over after that point.