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This paper proposes a framework for sentence-level interpretability of rubric-based scoring, comparing SHAP and LLM-generated rationales. It finds that fine-tuned pretrained language models outperform LLMs in prediction accuracy, and SHAP provides more faithful and transferable explanations.