Da Corte, MiguelBaptista, Jorge2026-03-312026-03-312025978-3-95977-387-4http://hdl.handle.net/10400.1/28579This study examines the intersection of sociodemographic characteristics, linguistic features, and writing placement outcomes at a community college in the United States of America. It focuses on 210 anonymized writing samples from native English speakers (L1) that were automatically classified by Accuplacer and independently assessed by two trained raters. Disparities across gender and race using 40 top-ranked linguistic features selected from Coh-Metrix, CTAP, and Developmental Education-Specific (DES) sets were analyzed. Three statistical tests were used: one-way ANOVA, Tukey’s HSD, and Chi-square. ANOVA results showed racial differences in nine linguistic features, especially those tied to syntactic complexity, discourse markers, and lexical precision. Gender differences were more limited, with only one feature reaching significance (Positive Connectives, p = 0.007). Tukey’s HSD pairwise tests showed no significant gender group variation but revealed sensitivity in DES features when comparing racial groups. Chi-square analysis indicated no significant association between gender and placement outcomes but suggested a possible link between race and human-assigned levels (χ 2 = 9.588, p = 0.048). These findings suggest that while automated systems assess general writing skills, human-devised linguistic features and demographic insights can support more equitable placement practices for all students entering college-level programs.engDevelopmental education (DevEd)Sociolinguistic variationText classificationMachine learningPlacement equitybook part10.4230/OASIcs.SLATE.2025.6