Alessandra Basso

The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article illustrates with an example how psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification.

This paper distinguishes between two arguments based on measurement robustness and defends the epistemic value of robustness for the assessment of measurement reliability. I argue that the appeal to measurement robustness in the assessment of measurement is based on a different inferential pattern and is not exposed to the same objections as the no-coincidence argument which is commonly associated with the use of robustness to corroborate individual results. This investigation sheds light on the precise meaning of reliability that emerges from measurement assessment practice. In addition, by arguing that the measurement assessment robustness argument has similar characteristics across the physical, social and behavioural sciences, I defend the idea that there is continuity in the notion of measurement reliability across sciences.