  This paper describes a new breed of academic journals that use statistical
machine learning techniques to make them more democratic. In particular, not
only can anyone submit an article, but anyone can also become a reviewer.
Machine learning is used to decide which reviewers accurately represent the
views of the journal's readers and thus deserve to have their opinions carry
more weight. The paper concentrates on describing a specific experimental
prototype of a democratic journal called the Journal of New Democratic Methods
(JNDM). The paper also mentions the wider implications that machine learning
and the techniques used in the JNDM may have for representative democracy in
general.
