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Reputation systems could help.

Then you're simply increasing granularity...instead of judging papers by what journals considered them worthy, judge by what individuals considered them worthy...and if you don't know the individuals involved, the reputation system is there to help you.

It'd have to be something similar to pagerank, not a simple vote-count method. High-reputation individuals should be able to contribute more to the reputation of other individuals.

Another idea would be to apply pagerank to article citations, though this would only be useful for older papers.

Pagerank patent expires in 2018. There are some other interesting reputation system out there, too.



For programming reputation has a point, but for science articles, each should be judged on their own merit. Ideally you shouldn't know whose article it is that you're reviewing, or you run the risk of preferential treatment.


I wasn't thinking of judging articles by the reputation of the authors. Instead, I was thinking of judging the value of reviews by the reputation of the reviewers.

I think that's actually pretty similar to what we're doing now. Articles accepted by journals of high reputation get a high reputation, and the journals select high-reputation individuals to do reviews. A good distributed reputation algorithm could perform essentially the same function. Leaving review open to anyone could help preserve net objectivity even if some individual reviewers are biased.

But anonymity could be accomplished by releasing articles initially with a timestamp and a digital signature by a new public key. After review, the authors could reveal that they have the matching private key.




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