  We consider unsupervised crowdsourcing performance based on the model wherein
the responses of end-users are essentially rated according to how their
responses correlate with the majority of other responses to the same
subtasks/questions. In one setting, we consider an independent sequence of
identically distributed crowdsourcing assignments (meta-tasks), while in the
other we consider a single assignment with a large number of component
subtasks. Both problems yield intuitive results in which the overall
reliability of the crowd is a factor.
