Natural language processing word dictionaries for personnel selection: Achieving explainable, generalizable, and incremental prediction with low impact when passively scoring narrative application information.

= 67,652), including employment and school settings, experimental and operational contexts, concurrent and predictive designs, and a range of occupations. Results show consistent prediction of selection ratings and decisions and postselection performance with uncorrected grand averages across dictionaries and studies in the low .20 s for selection ratings and nearer .10 for performance criteria, incremental prediction beyond traditional assessments (mental ability and personality tests), measurement of constructs like motivation and personality without using self-scoring, and small gender and racioethnic subgroup differences. We close with theoretical and practical implications to show that dictionaries are explainable, generalizable, straightforward to content validate, useful for passively scoring information already gathered, accessible to researchers with typical statistical skills, can add incremental prediction to common existing measures, can capture previously difficult-to-measure constructs like motivation, and may help address the validity-adverse impact trade-off. (PsycInfo Database Record (c) 2026 APA, all rights reserved).