Collaborative research, Wikipedia or elsewhere

There is a post at the Machine Learning (Theory) blog which discusses online collaborative research and how the right system could take the lead with well-designed mechanisms for credit and controversy.  There was a previous post on this blog December 2005 that noticed the progress year over year of the Machine Learning page at Wikipedia, suggesting a possible future:

Wikipedia becomes better to the point where it is a completely comprehensive listing of current research in machine learning. At some point, we-the-community realize this and begin to emphasize (and credit) information placed in wikipedia.

The more recent post at MLT lists necessary ingredients for an online collaboration system for scientific research, highlighting dicussion, credit, and disagreement mechanisms.

There does exist a discussion framework on Wikipedia/MLpedia, but the presentation format marginalizes discussion, placed on a separate page and generally not viewed by most observers. The discussion, in fact, should be an integral part of the presentation.
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Wikipedia intentionally anonymizes contributors in the presentation, because recognizing them might invite the wrong sort of contributor. Incentives done well, however, are one of the things creating [community buy-in]. One of the existing constraints within academia is that the basic unit of credit is coauthorship on a peer-reviewed paper. Given this constraint, it would be very handy if a system could automatically translate a subset of an online site into a paper, with authorship automatically summarized.

A comment by Aaron Hertzmann suggests how online collaboration could make life easier for people doing research.  He breaks down the function of a discussion page for papers new and old:

With more recent papers, we want to have discussion in order to understand the work and its significance, and to hear what other people think of the paper. Often, the most interesting aspects come out later in discussions with colleagues, and its a matter of luck as to whether these discussions happen (and whether you happen to be present). With older papers, its really useful to find out the history and signficance of the paper — the oral tradition associated with the paper — which is otherwise not written down. The authors (or other implementers) often have hindsights about the work which are not written in the paper.

Also related and good to read: (via BoingBoing) Clay Shirky's recent remarks on experts, Wikipedia and Larry Sanger's Citizendium.