Safety judges are typically optimized for static harm definitions, yet real-world deployment policies evolve rapidly. We present , a model-agnostic safety judging framework that enables zero-shot policy adaptation without model fine-tuning. Rather than relying on static weights or vector-search RAG over raw text chunks, CourtGuard-PMPD introduces a system abstraction that compiles external governance documents into a Prompt-Based Modular Policy Database (PMPD). At inference time, an LLM category mapper routes inputs against active policy modules to lightweight label judges. When policies change, the system updates its decision boundary instantly by swapping database modules. Evaluated on MLCommons intra-domain policy adaptation, CourtGuard-PMPD achieves the highest mean accuracy (0.84) and F1 (0.82) across four staged policy removals, outperforming policy-following baselines without retraining. On a policy-aligned 850-example attack suite and an eight-dataset public benchmark suite, it remains highly competitive (0.915 and 0.871 accuracy, respectively). These results position policy-grounded judging as a controllable and competitive alternative to static safety classifiers and benchmark-oriented generic judges.

Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.