Scientific curation, where scientific evidence is selected and shared, is essential to public belief formation about science. Yet common curation practices can distort the body of evidence the public sees. Focusing on science journalism, we employ computational models to investigate how such distortions influence public belief. We consider these effects for agents with and without confirmation bias. We find that standard journalistic practices can lead to significant distortions in public belief; that preexisting errors in public belief can drive further distortions in reporting; that practices that appear relatively unobjectionable can produce serious epistemic harm; and that, in some cases, common curation practices related to fairness and extreme reporting can lead to polarization.