  Fairness in multiwinner elections, a growing line of research in
computational social choice, primarily concerns the use of constraints to
ensure fairness. Recent work proposed a model to find a diverse \emph{and}
representative committee and studied the model's computational aspects.
However, the work gave complexity results under major assumptions on how the
candidates and the voters are grouped. Here, we close this gap and classify the
complexity of finding a diverse and representative committee using a monotone,
separable positional multiwinner voting rule, conditioned \emph{only} on the
assumption that P $\neq$ NP.
