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Using generations to implement random linear network coding garners benefits such as reduced decoding complexity. However, these benefits can come at the expense of throughput. In this paper, we seek to understand and maximize throughput for generation-based network coding (GBNC). Motivated by the application of network coding to scalable multicast, we consider schemes which result in high probability of decoding success with minimal feedback. We show that the throughput performance of GBNC is highly dependent on the choice of coding parameters and that GBNC becomes advantageous only when the number of source packet exceeds a network-dependent threshold. Results for various network topologies lead to the formulation of throughput-motivated guidelines for the adoption of GBNC.