Machine learning techniques apply to relations between objects (e.g. job offers) or categories of objects (e.g occupations). Social sciences often investigate category-to-category relations (e.g. is one occupation more prestigious than another?), whereas supervised learning applies mostly to object-to-category relations (e.g. which occupation is that offer about?). In this talk, I show how the latter can contribute to the former. First, I present the methodology of Frick et al. (2025) to obtain a meaningful representation of occupations in terms of skills from a corpus of job offers. Predicting the occupation of an offer serves as a pretext task to train simultaneously a position for the occupations in a 20-dimensional space and a function mapping job offers to that space. Second, I discuss the quantitative and qualitative validation of their representation. The resulting distance between occupations generalizes better than available alternatives based on structured fields. Third, I investigate the mathematical properties of their methodology. I study the type of category-to-category relations and metrics that can be rationalized with this representation. I also relate cosine distances between categories trained using the pretext task and misclassification errors in the training dataset.