Researchers routinely infer gender, race, and ethnicity from names, yet the standard tools used for this task misclassify the groups that diversity research most needs to measure. Large language models (LLMs) have been proposed as a remedy because they label diverse names accurately. We ask whether that accuracy carries over to the estimates researchers report. Across 10,000 Chinese names, 19,998 North Carolina voters with self-reported race, and 300 political science authors with human reference codes, we compare three LLMs with seven traditional tools and link the voter sample to party registration and age. Selective labeling explains part of the LLMs' accuracy advantage. Claude Haiku labels half of the voters, and a traditional tool limited to its most confident half is more accurate. Hard labels from every tool understate the White--Black gap in Democratic registration by 36% to 65%, while BIRDiE estimation on BISG posteriors comes within 0.03 of it. Haiku's missing labels select on age, inflating the age gap by six years. Combined with BISG, LLM answers sharpen individual labels without a demonstrated gain in BIRDiE accuracy. Classifiers should be judged by the estimates they produce, including the consequences of missing labels
Multimodal large language models (MLLMs) allow social scientists to analyze video at scale, but researchers must decide which parts of a video to provide as input. We use the term representation validity to examine how text, audio, video, and combined inputs affect measurement and subsequent analysis. We compare input representations across six open-weight MLLMs and three human-annotated video datasets. No representation consistently performs best. On seven news-related constructs, native-video performance varies little across models (macro-F1 = .70-.77), while extracted-text performance ranges from .52 to .80. Three models perform better with text and three with video. Combining inputs rarely improves performance. Higher classification accuracy also does not reliably produce estimates closer to those based on human codes. The highest-F1 representation best reproduces human-coded regression coefficients in 17 of 42 comparisons. Even when each model uses its highest-F1 deployable representation, 45% of sample-prevalence estimates differ from the human-coded benchmark by more than ten percentage points. Input representation therefore requires validation for both the construct being measured and the quantity estimated from the resulting labels.