Scalar inferences occur when a weaker statement like *It’s warm* is used when a stronger one like *It’s hot* could have been used instead, resulting in the inference that whoever produced the weaker statement believes that the stronger statement does not hold. The rate at which this inference is drawn varies across scalar words, a result termed “scalar diversity”. Here, we study scalar diversity in adjectival scalar words from a usage-based perspective. We introduce novel operationalisations of several previously-observed predictors of scalar diversity using computational tools based on usage data, allowing us to move away from existing judgment-based methods. In addition, we show in two experiments that, above and beyond these previously-observed predictors, scalar diversity is predicted in part by the relevance of the scalar inference at hand. We introduce a corpus-based measure of relevance based on the idea that scalar inferences that are more relevant are more likely to occur in scalar constructions that draw an explicit contrast between scalar words (e.g., *It’s warm but not hot*). We conclude that usage has an important role to play in the establishment of common ground, a requirement for pragmatic inferencing. Data from Gotzer et al. (2018) has been included in this repository with permission.