  Motivated by the intricacies of allocating treasury funds in blockchain
settings, we study the problem of crowdsourcing reviews for many different
proposals, in parallel. During the reviewing phase, every reviewer can select
the proposals to write reviews for, as well as the quality of each review. The
quality levels follow certain very coarse community guidelines and can have
values such as 'excellent' or 'good'. Based on these scores and the
distribution of reviews, every reviewer will receive some reward for their
efforts. In this paper, we design a reward scheme and show that it always has
pure Nash equilibria, for any set of proposals and reviewers. In addition, we
show that these equilibria guarantee constant factor approximations for two
natural metrics: the total quality of all reviews, as well as the fraction of
proposals that received at least one review, compared to the optimal outcome.
