This thesis proposal addresses methodological gaps in applying NLP to social science by shifting from categorical classification to comparative scaling of grounded constructs. We first extend predictive capacity on existing specialized political datasets with prompt optimization and distillation approaches. We then develop an active learning framework for efficient comparative annotation to scale latent dimensions from large corpora. Finally, we apply this pipeline to measure benevolent sexism in Slovenian media and migration threat perception in parliamentary discourse. This work establishes a scalable workflow for moving NLP from ad-hoc classification to theoretically grounded comparative measurement.