Understanding the sociodemographic composition of online platforms is essential for accurately interpreting digital behavior and its societal implications. Yet, current methods often lack the transparency and reliability required, risking misrepresenting social identities and distorting our understanding of digital society. Here, we introduce a principled framework for sociodemographic inference on Reddit that leverages over 850,000 user self-declarations of age, gender, and partisan affiliation. By training models on sparse user activity signals from this extensive, self-disclosed dataset, we demonstrate that simple probabilistic models, such as Naive Bayes, outperform more complex embedding-based alternatives. Our approach improves classification performance over the state of the art by up to 19% in ROC AUC and maintains quantification error below 15%. The models produce well-calibrated and interpretable outputs, enabling uncertainty estimation and subreddit-level feature importance analysis. More broadly, this work advocates for a shift toward more ethical and transparent computational social science by grounding sociodemographic analysis in user-provided data rather than researcher assumptions.