Online discussions increasingly serve as a major venue for exchanging information and evaluating competing viewpoints. Yet most computational approaches to discourse quality focus on detecting harmful language or predicting engagement, providing limited insight into whether interactions actually improve collective understanding.We introduce a two-dimensional framework for modeling dialogic constructiveness, distinguishing between substantive contribution (SC) and relational conduct (SC). Using expert-annotated Ukrainian-language discussions, we show that collapsing rubric-level labels into these axes improves inter-annotator agreement, suggesting that constructiveness is better captured as a multidimensional judgment.We further compare nominal, regression, and ordinal prediction approaches and find that explicitly modeling constructiveness as an ordinal task yields substantially higher agreement with expert annotations under quadratic weighted kappa (QWK). These results indicate that dialogic constructiveness is better understood as an ordered interactional judgment rather than a binary label or continuous score.