In the course of legal reasoning -whether for purposes of deciding an issue, justifying a decision, predicting how an issue will be decided, or arguing for how it should be decided -one often is required to reach (and assert) conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI & Law work has modeled reason-balancing, both within and across cases, with settheoretic and rule-or value-ordering approaches. This article explores how modeling it in 'choiceboxing' terms may yield new questions, insights, and tools.