Alignment may encourage a model to commit to one answer even when human judgments differ. We study this effect by comparing base and aligned checkpoints on ChaosNLI and Measuring Hate Speech using logit, sampled, and verbalized elicitation. Across eight model pairs from 7B to 32B, including a mixture-of-experts model, alignment lowers the entropy of logit-elicited label distributions. Sampled elicitation agrees on the three pairs tested. The effect survives prompt-format, answer-order, word-only, and task-compliance controls. Its implications for human fit are less straightforward: Jensen–Shannon distance worsens on ChaosNLI, but changes in both directions on the native MHS hate-speech task. Six of seven aligned checkpoints in the main sweep fit ChaosNLI annotations worse than a uniform predictor, yet temperature scaling to base mean entropy removes much of the base–aligned gap. Majority-label agreement also changes in both directions. Verbalized probabilities show a different pattern, with entropy increases and decreases across three families and substantial sensitivity to the prompt’s numeric example in some base checkpoints. Staged comparisons of two Ai2 lineages further show that the contributions of SFT and DPO depend on task and prompt format. These results support a consistent sharpening effect under logit and sampled elicitation, while showing why entropy alone cannot establish whether alignment reduces a model’s ability to represent human disagreement.