Before revision:  A receiver tries to predict a latent characteristic of the sender . The sender has a vector of features, which can be distorted at a cost. An intermediary observes the features of the sender and assigns a linear score, which is passed to the receiver. The optimal scoring rule downweights features on which gaming is heterogeneous. Optimal scoring discourages sender manipulation and allows the receiver to make more accurate predictions than full transparency .
After revision:  A receiver prefers this score to seeing the sender's full features because the coarser information mitigates his commitment problem .