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However, evaluation protocols for such data were designed for text and have not been validated for speech. We present the first ICC-based, controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts. We show that achieving $\\textit{good}$ agreement within either modality (ICC(2,$k$) $\\approx$ .80) requires $\\sim$9 raters. At the same time, modalities show marked differences in how people report preferences: audio raters exhibit narrower decision thresholds, reduced length bias, and more user-oriented evaluation criteria, with near-chance cross-modality agreement. 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At the same time, modalities show marked differences in how people report preferences: audio raters exhibit narrower decision thresholds, reduced length bias, and more user-oriented evaluation criteria, with near-chance cross-modality agreement. 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