We model the effect of networks on the uptake of risky innovations by workers and the policy-making of their managers as a Stackelberg game. We specifically examine (networked) semi-anonymous (SA) whistleblowing policies, where workers can report specific observed instances of unsanctioned behavior to management. This setting is labeled semi-anonymous in contrast with the anonymous case, where only the existence of such behavior is reported without mention of potential culprits. We compare the subgame-perfect equilibria of the SA Stackelberg game for general networks and for regular graphs with the corresponding equilibria of the anonymous whistleblowing case, in which whistleblowing has been shown to only flourish in a light-punishment regime. We observe that SA whistleblowing can lead to better equilibrium outcomes for the manager, as the network structure induces a heterogeneity in responses from the workers which can be exploited by the manager to maintain moderate amounts of unsanctioned, but potentially beneficial, behavior. Workers will exhibit different behavior depending on how many peers they observe and are observed by, cheating if the audit probability is below a specific threshold and reporting observed cheating otherwise. Without this network-induced behavioral heterogeneity (e.g., for regular graphs), SA whistleblowing equilibria resemble those arising from anonymous policies. Finally, we show that in a light-punishment regime, workers with the fewest neighbors will be the most likely to cheat.